Hr Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>education</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65438</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_7</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65141</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7513</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_19</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2542</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48945</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_26</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id         department     region         education gender  \\\n",
       "0        65438  Sales & Marketing   region_7  Master's & above      f   \n",
       "1        65141         Operations  region_22        Bachelor's      m   \n",
       "2         7513  Sales & Marketing  region_19        Bachelor's      m   \n",
       "3         2542  Sales & Marketing  region_23        Bachelor's      m   \n",
       "4        48945         Technology  region_26        Bachelor's      m   \n",
       "\n",
       "  recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "0            sourcing                1   35                   5.0   \n",
       "1               other                1   30                   5.0   \n",
       "2            sourcing                1   34                   3.0   \n",
       "3               other                2   39                   1.0   \n",
       "4               other                1   45                   3.0   \n",
       "\n",
       "   length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
       "0                  8              1            0                  49   \n",
       "1                  4              0            0                  60   \n",
       "2                  7              0            0                  50   \n",
       "3                 10              0            0                  50   \n",
       "4                  2              0            0                  73   \n",
       "\n",
       "   is_promoted  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "employee_id                0\n",
       "department                 0\n",
       "region                     0\n",
       "education               2409\n",
       "gender                     0\n",
       "recruitment_channel        0\n",
       "no_of_trainings            0\n",
       "age                        0\n",
       "previous_year_rating    4124\n",
       "length_of_service          0\n",
       "KPIs_met >80%              0\n",
       "awards_won?                0\n",
       "avg_training_score         0\n",
       "is_promoted                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('employee_id',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['department', 'region', 'education', 'gender', 'recruitment_channel',\n",
       "       'no_of_trainings', 'age', 'previous_year_rating', 'length_of_service',\n",
       "       'KPIs_met >80%', 'awards_won?', 'avg_training_score', 'is_promoted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    91.482995\n",
       "1     8.517005\n",
       "Name: is_promoted, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['is_promoted'].value_counts()/df.shape[0]*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since the class ratio is very less and not following 70-30 rule so we would consider it as an imbalenced dataset and treat accordingly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAFVCAYAAAANA4MgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dfZxcdX328c9FeFSBBFgUk2BSDUpECBIxFbyLoBBADVZQ8IEUaWMtVOltreBti6C0YltUvJW7USLBKjGillSCNCI+CyRICISHZg0oIZQEQjCCBQPX/cf5rU6XSXZ2N5kzm7ner9e8ds73nDPzHZbsNed3nmSbiIjobtvV3UBERNQvYRAREQmDiIhIGEREBAmDiIggYRARESQMYoSTdISkVTW874ckfaHd7xuxtSQMIgbQLHBs/73tP91K77ePpEslPSBpg6S7JJ0n6dlb4/0a3vcjkv51a75HdK6EQUQHkbQH8FNgF+APbe8KvA4YDbywzt5i25YwiI4j6fmSvi5praR7JL23Yd4uki6T9IikO4BX9FvXkl7UMH2ZpI81TM+QtFTSryT9XNL0Uj9N0p3lm/hKSe8u9WcD1wDPl/Tr8nh+/2/Rkt4oabmk9ZK+J2n/hnn3SvprScskPSrpq5J23sTH/9/ABuAdtu8FsH2f7ffZXlZe71WSFpfXWizpVf3e67UN07/rU9KE8t9npqRfSnpI0v8p86YDHwLeWj7jrS39smKbkTCIjiJpO+DfgVuBscBRwFmSjimLnEv1DfmFwDHAzEG89qHA5cAHqL5p/y/g3jJ7DfB6YDfgNOCTkl5u+zHgWGC17eeUx+p+r7sfcAVwFtADLAT+XdKODYu9BZgOTAQOBP5kE22+FviG7ac38Rn2AK4GLgb2BC4Crpa0Z4v/GQAOB15M9d/27yTtb/vbwN8DXy2f8aBBvF5sAxIG0WleAfTYPt/2k7ZXAp8HTi7z3wJcYHud7fuo/ii26nRgju1Ftp+2fb/tuwBsX2375658H/gP4NUtvu5bgavL6/4W+CeqYZ5XNSxzse3VttdRhd2UTbzWnsADm3mv44EVtr9ke6PtK4C7gDe02CvAebZ/Y/tWqtDNH/5g+7obiOjnBVRDMusbaqOAH5bnzwfua5j3i0G89niqb+3PIOlYqq2O/ai+JD0LuK3F131+Yx+2n5Z0H9WWTZ//anj+eFmnmYeBfVp9r+IX/d5rIP17ec4g1o1tVLYMotPcB9xje3TDY1fbx5X5D1D9Ue+zb7/1H6f6Q97nef1e+xk7YSXtBHyd6hv9c22PpgoNlUUGurTvaqoQ63s9lR7vH2C9Zr4DvKkMlw34XsW+De/1GJv+/APJJYy7WMIgOs1NwK8kfbDsLB4l6QBJfTuK5wPnSBojaRzwl/3WXwq8raw3HfijhnmXAqdJOkrSdpLGSnoJsCOwE7AW2Fi2Eo5uWO9BYE9Ju2+i5/nA8eV1dwDeDzwB/GQIn/8iqv0WcyW9AKD0eZGkA6lCaj9Jb5O0vaS3ApOBbzV8/pMl7SBpKnDiIN77QWDCZoIotmH5pUdHsf0U1fj3FOAe4CHgC0DfH+LzqIZF7qEa1/9Sv5d4X1l/PfB24N8aXvsmys5h4FHg+8ALbG8A3kv1R/0R4G3Agob17qLaQbyyHC30P4Z4bN8NvAP4TOn3DcAbbD85hM+/jmpfw2+BGyVtAK4r/fbafphqR/f7qYaU/gZ4ve2Hykv8LdXWzyPlv9VXBvH2Xys/H5b0s8H2HiObcnObiIjIlkFERCQMIiIiYRARESQMIiKChEFERDCCz0Dea6+9PGHChLrbiIgYUW6++eaHbPf0r4/YMJgwYQJLliypu42IiBFFUtNLuLQ8TFTO6LxF0rfK9ERJN0paUS7Ju2Op71Sme8v8CQ2vcU6p391wFUokTS+1XklnD/VDRkTE0Axmn8H7gDsbpi8EPml7EtXZjqeX+unAI7ZfRHWm54UAkiZTXXnypVSX8v1cCZhRwGepLhM8GTilLBsREW3SUhiUa8AcT3VZgL4LcR0JXFkWmQucUJ7PKNOU+UeV5WcA82w/YfseoBc4tDx6ba8sp+/PK8tGRESbtLpl8Cmqa6D03XBjT2C97Y1lehW/v4TuWMolhsv8R8vyv6v3W2dT9WeQNEvSEklL1q5d22LrERExkAHDQNLrgTW2b24sN1nUA8wbbP2ZRXu27am2p/b0PGNneEREDFErRxMdBrxR0nHAzlSX1/0UMFrS9uXb/ziq66xD9c1+PLBK0vZUV5tc11Dv07jOpuoREdEGA24Z2D7H9jjbE6h2AH/X9tuB6/n9tdJnAleV5wv4/X1pTyzLu9RPLkcbTQQmUV27fjEwqRydtGN5j99dPjgiIra+4Zxn8EFgnqSPAbdQ3TiE8vNLknqptghOBrC9XNJ84A5gI3BGuXY9ks4ErqW6veEc28uH0VdERAzSiL2fwdSpU93Ok84mnH11296r3e79+PF1txARbSLpZttT+9dzbaKIiEgYREREwiAiIkgYREQECYOIiCBhEBERJAwiIoKEQUREkDCIiAgSBhERQcIgIiJIGEREBAmDiIggYRARESQMIiKChEFERJAwiIgIWggDSTtLuknSrZKWSzqv1C+TdI+kpeUxpdQl6WJJvZKWSXp5w2vNlLSiPGY21A+RdFtZ52JJ2hofNiIimmvlHshPAEfa/rWkHYAfSbqmzPuA7Sv7LX8s1c3uJwGvBC4BXilpD+BcYCpg4GZJC2w/UpaZBdwALASmA9cQERFtMeCWgSu/LpM7lMfmbpw8A7i8rHcDMFrSPsAxwCLb60oALAKml3m72f6pqxsyXw6cMIzPFBERg9TSPgNJoyQtBdZQ/UG/scy6oAwFfVLSTqU2FrivYfVVpba5+qom9YiIaJOWwsD2U7anAOOAQyUdAJwDvAR4BbAH8MGyeLPxfg+h/gySZklaImnJ2rVrW2k9IiJaMKijiWyvB74HTLf9QBkKegL4InBoWWwVML5htXHA6gHq45rUm73/bNtTbU/t6ekZTOsREbEZrRxN1CNpdHm+C/Ba4K4y1k858ucE4PayygLg1HJU0TTgUdsPANcCR0saI2kMcDRwbZm3QdK08lqnAldt2Y8ZERGb08rRRPsAcyWNogqP+ba/Jem7knqohnmWAn9ell8IHAf0Ao8DpwHYXifpo8Distz5tteV5+8BLgN2oTqKKEcSRUS00YBhYHsZcHCT+pGbWN7AGZuYNweY06S+BDhgoF4iImLryBnIERGRMIiIiIRBRESQMIiICBIGERFBwiAiIkgYREQECYOIiCBhEBERJAwiIoKEQUREkDCIiAgSBhERQcIgIiJIGEREBAmDiIggYRARESQMIiKCFsJA0s6SbpJ0q6Tlks4r9YmSbpS0QtJXJe1Y6juV6d4yf0LDa51T6ndLOqahPr3UeiWdveU/ZkREbE4rWwZPAEfaPgiYAkyXNA24EPik7UnAI8DpZfnTgUdsvwj4ZFkOSZOBk4GXAtOBz0kaJWkU8FngWGAycEpZNiIi2mTAMHDl12Vyh/IwcCRwZanPBU4oz2eUacr8oySp1OfZfsL2PUAvcGh59NpeaftJYF5ZNiIi2qSlfQblG/xSYA2wCPg5sN72xrLIKmBseT4WuA+gzH8U2LOx3m+dTdWb9TFL0hJJS9auXdtK6xER0YKWwsD2U7anAOOovsnv32yx8lObmDfYerM+ZtueantqT0/PwI1HRERLBnU0ke31wPeAacBoSduXWeOA1eX5KmA8QJm/O7Cusd5vnU3VIyKiTVo5mqhH0ujyfBfgtcCdwPXAiWWxmcBV5fmCMk2Z/13bLvWTy9FGE4FJwE3AYmBSOTppR6qdzAu2xIeLiIjWbD/wIuwDzC1H/WwHzLf9LUl3APMkfQy4Bbi0LH8p8CVJvVRbBCcD2F4uaT5wB7AROMP2UwCSzgSuBUYBc2wv32KfMCIiBjRgGNheBhzcpL6Sav9B//p/Aydt4rUuAC5oUl8ILGyh34iI2ApyBnJERCQMIiIiYRARESQMIiKChEFERJAwiIgIEgYREUHCICIiSBhERAQJg4iIIGEQEREkDCIigoRBRESQMIiICBIGERFBwiAiIkgYREQErd0Debyk6yXdKWm5pPeV+kck3S9paXkc17DOOZJ6Jd0t6ZiG+vRS65V0dkN9oqQbJa2Q9NVyL+SIiGiTVrYMNgLvt70/MA04Q9LkMu+TtqeUx0KAMu9k4KXAdOBzkkaVeyh/FjgWmAyc0vA6F5bXmgQ8Apy+hT5fRES0YMAwsP2A7Z+V5xuAO4Gxm1llBjDP9hO27wF6qe6VfCjQa3ul7SeBecAMSQKOBK4s688FThjqB4qIiMEb1D4DSROAg4EbS+lMScskzZE0ptTGAvc1rLaq1DZV3xNYb3tjv3pERLRJy2Eg6TnA14GzbP8KuAR4ITAFeAD4575Fm6zuIdSb9TBL0hJJS9auXdtq6xERMYCWwkDSDlRB8GXb3wCw/aDtp2w/DXyeahgIqm/24xtWHwes3kz9IWC0pO371Z/B9mzbU21P7enpaaX1iIhoQStHEwm4FLjT9kUN9X0aFnsTcHt5vgA4WdJOkiYCk4CbgMXApHLk0I5UO5kX2DZwPXBiWX8mcNXwPlZERAzG9gMvwmHAO4HbJC0ttQ9RHQ00hWpI517g3QC2l0uaD9xBdSTSGbafApB0JnAtMAqYY3t5eb0PAvMkfQy4hSp8IiKiTQYMA9s/ovm4/sLNrHMBcEGT+sJm69leye+HmSIios1yBnJERCQMIiIiYRARESQMIiKChEFERJAwiIgIEgYREUHCICIiSBhERAQJg4iIIGEQEREkDCIigoRBRESQMIiICBIGERFBwiAiIkgYREQECYOIiKCFMJA0XtL1ku6UtFzS+0p9D0mLJK0oP8eUuiRdLKlX0jJJL294rZll+RWSZjbUD5F0W1nnYknNbrMZERFbSStbBhuB99veH5gGnCFpMnA2cJ3tScB1ZRrgWGBSecwCLoEqPIBzgVdS3e/43L4AKcvMalhv+vA/WkREtGrAMLD9gO2flecbgDuBscAMYG5ZbC5wQnk+A7jclRuA0ZL2AY4BFtleZ/sRYBEwvczbzfZPbRu4vOG1IiKiDQa1z0DSBOBg4EbgubYfgCowgL3LYmOB+xpWW1Vqm6uvalJv9v6zJC2RtGTt2rWDaT0iIjaj5TCQ9Bzg68BZtn+1uUWb1DyE+jOL9mzbU21P7enpGajliIhoUUthIGkHqiD4su1vlPKDZYiH8nNNqa8CxjesPg5YPUB9XJN6RES0SStHEwm4FLjT9kUNsxYAfUcEzQSuaqifWo4qmgY8WoaRrgWOljSm7Dg+Gri2zNsgaVp5r1MbXisiItpg+xaWOQx4J3CbpKWl9iHg48B8SacDvwROKvMWAscBvcDjwGkAttdJ+iiwuCx3vu115fl7gMuAXYBryiMiItpkwDCw/SOaj+sDHNVkeQNnbOK15gBzmtSXAAcM1EtERGwdOQM5IiISBhERkTCIiAgSBhERQcIgIiJIGEREBAmDiIggYRARESQMIiKChEFERJAwiIgIEgYREUHCICIiSBhERAQJg4iIIGEQEREkDCIigtbugTxH0hpJtzfUPiLpfklLy+O4hnnnSOqVdLekYxrq00utV9LZDfWJkm6UtELSVyXtuCU/YEREDKyVLYPLgOlN6p+0PaU8FgJImgycDLy0rPM5SaMkjQI+CxwLTAZOKcsCXFheaxLwCHD6cD5QREQM3oBhYPsHwLqBlitmAPNsP2H7HqAXOLQ8em2vtP0kMA+YIUnAkcCVZf25wAmD/AwRETFMw9lncKakZWUYaUypjQXua1hmValtqr4nsN72xn71iIhoo+2HuN4lwEcBl5//DLwLUJNlTfPQ8WaWb0rSLGAWwL777ju4jqNrTTj76rpb2Kru/fjxdbcQ24AhbRnYftD2U7afBj5PNQwE1Tf78Q2LjgNWb6b+EDBa0vb96pt639m2p9qe2tPTM5TWIyKiiSGFgaR9GibfBPQdabQAOFnSTpImApOAm4DFwKRy5NCOVDuZF9g2cD1wYll/JnDVUHqKiIihG3CYSNIVwBHAXpJWAecCR0iaQjWkcy/wbgDbyyXNB+4ANgJn2H6qvM6ZwLXAKGCO7eXlLT4IzJP0MeAW4NIt9ukiIqIlA4aB7VOalDf5B9v2BcAFTeoLgYVN6iv5/TBTRETUIGcgR0REwiAiIhIGERFBwiAiIkgYREQECYOIiCBhEBERJAwiIoKEQUREkDCIiAgSBhERQcIgIiJIGEREBAmDiIggYRARESQMIiKChEFERJAwiIgIWggDSXMkrZF0e0NtD0mLJK0oP8eUuiRdLKlX0jJJL29YZ2ZZfoWkmQ31QyTdVta5WJK29IeMiIjNa2XL4DJger/a2cB1ticB15VpgGOBSeUxC7gEqvAAzgVeSXW/43P7AqQsM6thvf7vFRERW9mAYWD7B8C6fuUZwNzyfC5wQkP9clduAEZL2gc4Blhke53tR4BFwPQybzfbP7Vt4PKG14qIiDYZ6j6D59p+AKD83LvUxwL3NSy3qtQ2V1/VpN6UpFmSlkhasnbt2iG2HhER/W3pHcjNxvs9hHpTtmfbnmp7ak9PzxBbjIiI/oYaBg+WIR7KzzWlvgoY37DcOGD1APVxTeoREdFGQw2DBUDfEUEzgasa6qeWo4qmAY+WYaRrgaMljSk7jo8Gri3zNkiaVo4iOrXhtSIiok22H2gBSVcARwB7SVpFdVTQx4H5kk4HfgmcVBZfCBwH9AKPA6cB2F4n6aPA4rLc+bb7dkq/h+qIpV2Aa8ojIiLaaMAwsH3KJmYd1WRZA2ds4nXmAHOa1JcABwzUR0REbD05AzkiIhIGERGRMIiICBIGERFBwiAiIkgYREQECYOIiCBhEBERJAwiIoKEQUREkDCIiAgSBhERQcIgIiJIGEREBAmDiIggYRARESQMIiKCYYaBpHsl3SZpqaQlpbaHpEWSVpSfY0pdki6W1CtpmaSXN7zOzLL8CkkzN/V+ERGxdWyJLYPX2J5ie2qZPhu4zvYk4LoyDXAsMKk8ZgGXQBUeVPdVfiVwKHBuX4BERER7bI1hohnA3PJ8LnBCQ/1yV24ARkvaBzgGWGR7ne1HgEXA9K3QV0REbMJww8DAf0i6WdKsUnuu7QcAys+9S30scF/DuqtKbVP1iIhok+2Huf5htldL2htYJOmuzSyrJjVvpv7MF6gCZxbAvvvuO9heIyJiE4a1ZWB7dfm5Bvgm1Zj/g2X4h/JzTVl8FTC+YfVxwOrN1Ju932zbU21P7enpGU7rERHRYMhhIOnZknbtew4cDdwOLAD6jgiaCVxVni8ATi1HFU0DHi3DSNcCR0saU3YcH11qERHRJsMZJnou8E1Jfa/zFdvflrQYmC/pdOCXwEll+YXAcUAv8DhwGoDtdZI+Ciwuy51ve90w+oqIiEEachjYXgkc1KT+MHBUk7qBMzbxWnOAOUPtJSIihidnIEdERMIgIiISBhERQcIgIiJIGEREBAmDiIggYRARESQMIiKChEFERJAwiIgIEgYREUHCICIiSBhERAQJg4iIIGEQEREkDCIigoRBREQwvNteRkRsdRPOvrruFraqez9+fN0tAB20ZSBpuqS7JfVKOrvufiIiuklHhIGkUcBngWOBycApkibX21VERPfoiDAADgV6ba+0/SQwD5hRc08REV2jU/YZjAXua5heBbyy/0KSZgGzyuSvJd3dht7qshfwUDveSBe24126Stt+d5Df31awrf/+XtCs2ClhoCY1P6NgzwZmb/126idpie2pdfcRg5ff3cjWrb+/ThkmWgWMb5geB6yuqZeIiK7TKWGwGJgkaaKkHYGTgQU19xQR0TU6YpjI9kZJZwLXAqOAObaX19xW3bpiOGwbld/dyNaVvz/Zzxiaj4iILtMpw0QREVGjhEFERCQMIrYESbtIenHdfUQMVcIgYpgkvQFYCny7TE+RlKPhRghJZ0oaU3cfdUsYdBBJh0l6dnn+DkkXSWp6tmB0lI9QXVJlPYDtpcCEGvuJwXkesFjS/HLBzGYnwW7zEgad5RLgcUkHAX8D/AK4vN6WogUbbT9adxMxNLY/DEwCLgX+BFgh6e8lvbDWxtosYdBZNro61ncG8GnbnwZ2rbmnGNjtkt4GjJI0SdJngJ/U3VS0rvy7+6/y2AiMAa6U9IlaG2ujhEFn2SDpHOAdwNXl0t471NxTDOwvgZcCTwBfAR4Fzqq1o2iZpPdKuhn4BPBj4GW23wMcAry51ubaKCeddRBJzwPeBiy2/UNJ+wJH2M5QUQeTdLDtW+ruI4ZG0nlUVz34RZN5+9u+s4a22i5hEDFMkq4H9gG+BszLpVRGDknbActsH1B3L3XLMFEHkHSPpJWSbqy7lxg8268BjgDWArMl3Sbpw/V2Fa2w/TRwa9kK72rZMojYgiS9jOpIsLfa3rHufmJgkr4LvAK4CXisr277jbU1VYOOuGppZHN1JJO0P/BW4CSqO2TNA95fa1MxGOfV3UAnSBh0CNtPS7pV0r62f1l3PzEoXwSuAF5nOzdlGmFsf7/uHjpBhok6SDZXR65yU6b9yuTdtn9bZz/ROknTgM8A+wM7Ut1T5THbu9XaWJtly6CzZHN1BJL0R1Rnit9LdT/v8ZJm2v5BrY1Fq/4v1d0VvwZMBU6lOiO5qyQMOojt70t6LtXWAcBNttfU2VO05CLgaNt3A0jaj2rY6JBau4qW2e6VNMr2U8AXJXXdGeQ5tLSDSHoL1RDRScBbgBslnVhvV9GCHfqCAMD2f5Izx0eSx8sw31JJn5D0V8Cz626q3bLPoINIupVqJ+SaMt0DfMf2QfV2FpsjaQ5g4Eul9HZge9un1ddVtKpcGXgNVYD/FbA78DnbvbU21mYJgw4i6TbbL2uY3g64tbEWnUfSTsAZwOFU+wx+QPXH5IlaG4sYhIRBB5H0j8CBVOPNUB27vsz2B+vrKlpRhhn2B56mOproyZpbigFIuo1qi64p2we2sZ3aJQw6jKQ3A4dRvmHa/mbNLcUAJB0P/D/g51S/t4nAu21fU2tjsVkNN446o/xsHOZ73Pb57e+qPgmDiGGSdBfw+r4x5nJTlKttv6TezqIVkn5s+7CBatu6HFraASRtoPnmqqjuu9FVJ7+MQGv67WxcSbVDMkaGZ0s63PaPACS9ii48mihh0AFs525mI5CkPy5Pl0taCMynCvWTgMW1NRaDdTowR9LuZXo98K4a+6lFhok6jKTDgUm2vyhpL2BX2/fU3Vc8k6Qvbma2bXfdH5SRTNJuVH8Tu/J+1gmDDiLpXKrT4V9sez9Jzwe+1m1jlxHtVA4NfjMwgYbRkm7bgZxhos7yJuBg4GcAtldLyhBSh5O0M9VQw0uBnfvq2TIYMa6ium/1zVT3se5KCYPO8qRtSzKApK7biTVCfQm4CzgGOJ/q0MSuuG/uNmKc7el1N1G3XJuos8yX9C/AaEl/BnwH+HzNPcXAXmT7b6kuezwXOB7IWeMjx0/KHeq6WrYMOojtf5L0OuBXwIuBv7O9qOa2YmB99y5YL+kA4L+oxp9jZDgc+BNJ91ANE/Ud0p0zkCOidZL+FPg61dbAZcBzgL+1/S919hWtaTgT+X+w/Yt291KnhEEHKcetXwjsTfXtJCedRbSBpIOAV5fJH9q+tc5+6pB9Bp3lE8Abbe9uezfbuyYIIrYuSe8Dvkz1JWxv4F8l/WW9XbVftgw6SDdeDyWibpKWAX9o+7Ey/Wzgp922zyA7kDtAw2UNlkj6KvBvNBzvbPsbtTQW0R0EPNUw/VSpdZWEQWd4Q8Pzx4GjG6YNJAxGAEnvARbYvl/SWbY/VXdP0ZIvUt1itu9y8ScAl9bYTy0yTBSxhUj6ADCdaqtulO1jam4pWiTp5TTcqc72LTW31HbZgdxBJM2VNLpheky5v250IEkzyvWj+vwT8J/AJKpDTWMEkDQNWGH7YtufBnolvbLuvtotYdBZDrS9vm/C9iNU1yqKzvRRqhPMkLQD8DXgSarzDXJdopHjEuDXDdOPlVpXSRh0lu0kjembkLQH2a/TyXYAKNfB/zZwi+332f5vYJdaO4vBkBvGy20/TRf+u+u6D9zh/pnqOilXlumTgAtq7Cc276tUF6jblerOZj+RJOCd5E5nI8lKSe/l91sDf0F1t7qukh3IHUbSS4HXUO3Ius72HTW3FJshaRywkWqn8aXAa4FbgVNzU6KRQdLewMXAkVRH710HnGW7qwI9YdCByv+cjdfF/2WN7UREF8g+gw4i6Y2SVgD3AN8H7gWuqbWpiG2cpP0kXSfp9jJ9oKQP191XuyUMOstHgWnAf9qeCBwF/LjeliK2eZ8HzqFcitz2MuDkWjuqQcKgs/zW9sNURxVtZ/t6YErdTUVs455l+6Z+tY21dFKjhEFnWS/pOcAPgC9L+jRd+D/lSCPppL57VUv6sKRvlDNaY2R4SNILqXYeI+lE4IF6W2q/7EDuIOVqib+hCum3A7sDXy5bC9GhJC2zfaCkw4F/oDoT+UO2u+4s1pFI0h8As4FXAY9Q7bN7e25uEx1B0l7Aw84vqONJusX2wZL+AbjN9lf6anX3Fq0rX8a2s72h7l7qkGGiDiBpmqTvleGFg8tRDbcDD0qaXnd/MaD7Jf0L8BZgoaSdyL+tjifpDf1uefl+4EeSFkiaWFdfdcmWQQeQtAT4ENWw0GzgWNs3SHoJcEW+YXY2Sc+iulrpbbZXSNoHeJnt/6i5tdiMclObabYfl/R64CLgFKrrgZ3UbVedTRh0AElLbU8pz++0vX/DvAw3RGwFkm61fVB5Pge42/aFZfpntrvqIIBsynaGpxue/6bfvKR1h5J0j6SVkm6su5cYEkl6jqTtqM7pua5h3s6bWGeblQvVdYaDJP2K6npEu5TnlOmu+59ypLA9sVyYbnzdvcSQfApYCvwKuNP2EgBJB5NDSyNisCTdbPuQuvuIwZM0FtgbuLVcupqyz2eHbrsmWLYMIobvBkmvsL247kZicGzfD9zfr9Z1WwWQLYOIYZN0B/BiqgsLPkRSXcMAAAQLSURBVEY1vGfbB9bZV8RgJAwihqnfseq/021nsMbIlqOJIoap/NEfDxxZnj9O/m2NGJLOl/S6cgZy18qWQcQwSToXmAq82PZ+kp4PfM32YTW3Fi2Q9C7gcOAPgQ3AD4Ef2L6q1sbaLGEQMUySllKdtfqzvhME+y5eV29nMRiSnkd1SZG/BsbY3rXmltoqRxNFDN+Tti2p7xLIXT3cMNJI+gIwGXiQaqvgROBntTZVg4xrRgzf/HKhutGS/gz4DvCFmnuK1u0JjALWA+uAh2x33X1EMkwUsQVIeh1wNNVhpdfaXlRzSzFIkvYHjgH+Chhle1zNLbVVhokihknShbY/CCxqUosOV65Y+mrgfwFjgO9SDRd1lWwZRAxTsytcZgfyyCHps1S3mv2h7dV191OXhEHEEEl6D/AXwB8AP2+YtSvwY9vvqKWxGDRJzwVeUSZvsr2mzn7qkDCIGCJJu1MNK/wDcHbDrA2219XTVQyWpJOo7lv9Pap9Pq8GPmD7yjr7areEQcQwSXohsMr2E5KOAA4ELre9vt7OohWSbgVe17c1IKkH+E7fjW+6RQ4tjRi+rwNPSXoRcCkwEfhKvS3FIGzXb1joYbrwb2OOJooYvqdtb5T0x8CnbH9G0i11NxUt+7aka4EryvRbgYU19lOLhEHE8P1W0inAqcAbSm2HGvuJQbD9AUlvBg6j2mcw2/Y3a26r7bLPIGKYJE0G/hz4qe0rJE0E3mr74zW3FtGyhEFEdCVJG4BmfwD7bk60W5tbqlXCIGKYJE2iOrx0MrBzX932H9TWVMQgdd0e84it4IvAJcBG4DXA5cCXau0oBkXS4ZJOK8/3KkN9XSVbBhHDJOlm24dIus32y0rth7ZfXXdvMbDcnKiSo4kihu+/JW0HrJB0JnA/sHfNPUXr3kS5ORGA7dWSuurGNpBhoogt4SzgWcB7gUOAdwIza+0oBuNJV0MkXX1zogwTRURXk/TXwCTgdVQHArwL+Irtz9TaWJslDCKGSNKCzc23/cZ29RLDk5sTJQwihkzSWuA+qssY3Ej1h+R3bH+/jr5i6CTtBTzsLvzDmH0GEUP3POBDwAHAp6mGGR6y/f0EQeeTNE3S9yR9Q9LBkm4HbgcelDS97v7aLVsGEVuApJ2AU4B/BM7vtvHmkUjSEqow3x2YDRxr+wZJLwGusH1wrQ22WcIgYhhKCBxPFQQTgAXAHNv319lXDEzSUttTyvM7be/fMO+WbguDnGcQMUSS5lINEV0DnGf79ppbisF5uuH5b/rN67pvydkyiBgiSU8Dj5XJxn9IXXmhs5FG0lNUvz8BuwCP980CdrbdVZchTxhERESOJoqIiIRBRESQMIiICBIGERFBwiAiIkgYREQE8P8BedfuT0IMRPwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cate_feat=['department', 'region', 'education', 'gender', 'recruitment_channel','no_of_trainings','previous_year_rating',\n",
    "        'KPIs_met >80%', 'awards_won?', 'is_promoted']\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "for i in cate_feat:\n",
    "    plt.title(i+' Count')\n",
    "    df[i].value_counts().plot(kind=\"bar\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a1620a7e88>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['age'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a16224c5c8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['avg_training_score'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in cate_feat[:-1]:\n",
    "    df.groupby(i)['is_promoted'].value_counts().unstack().plot(kind='bar',stacked=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.boxplot([df[df['is_promoted']==0]['age'],df[df['is_promoted']==1]['age']])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.subplot(121)\n",
    "plt.boxplot([df['age']])\n",
    "plt.xlabel('Age')\n",
    "plt.subplot(122)\n",
    "plt.boxplot([np.log(df['age'])])\n",
    "plt.xlabel('Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.subplot(121)\n",
    "plt.boxplot([df['previous_year_rating'].dropna()])\n",
    "plt.xlabel('previous_year_rating')\n",
    "plt.subplot(122)\n",
    "plt.boxplot([df[ 'length_of_service']])\n",
    "plt.xlabel( 'length_of_service')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filling missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a16370e408>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['department','gender'])['education'].value_counts().sort_index().unstack().plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By this we can see that almost all the education is on the basis of bachelor's so we would fill the missing value for education for bachelor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['education'].fillna(\"Bachelor's\",inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The value for previous_year_rating would be absent because there might be those people who haven't entered in the company. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['previous_year_rating'].fillna(0.0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df['age_log']=np.log(df['age'])\n",
    "df['no_of_trainings_log']=np.log(df['no_of_trainings'])\n",
    "df['avg_training_score_log']=np.log(df['avg_training_score'])\n",
    "df['length_of_service_log']=np.log(df['length_of_service'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>education</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "      <th>age_log</th>\n",
       "      <th>no_of_trainings_log</th>\n",
       "      <th>avg_training_score_log</th>\n",
       "      <th>length_of_service_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_7</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>3.555348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.891820</td>\n",
       "      <td>2.079442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Operations</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>3.401197</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.094345</td>\n",
       "      <td>1.386294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_19</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>3.526361</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.912023</td>\n",
       "      <td>1.945910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>3.663562</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>3.912023</td>\n",
       "      <td>2.302585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Technology</td>\n",
       "      <td>region_26</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>3.806662</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.290459</td>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          department     region         education gender recruitment_channel  \\\n",
       "0  Sales & Marketing   region_7  Master's & above      f            sourcing   \n",
       "1         Operations  region_22        Bachelor's      m               other   \n",
       "2  Sales & Marketing  region_19        Bachelor's      m            sourcing   \n",
       "3  Sales & Marketing  region_23        Bachelor's      m               other   \n",
       "4         Technology  region_26        Bachelor's      m               other   \n",
       "\n",
       "   no_of_trainings  age  previous_year_rating  length_of_service  \\\n",
       "0                1   35                   5.0                  8   \n",
       "1                1   30                   5.0                  4   \n",
       "2                1   34                   3.0                  7   \n",
       "3                2   39                   1.0                 10   \n",
       "4                1   45                   3.0                  2   \n",
       "\n",
       "   KPIs_met >80%  awards_won?  avg_training_score  is_promoted   age_log  \\\n",
       "0              1            0                  49            0  3.555348   \n",
       "1              0            0                  60            0  3.401197   \n",
       "2              0            0                  50            0  3.526361   \n",
       "3              0            0                  50            0  3.663562   \n",
       "4              0            0                  73            0  3.806662   \n",
       "\n",
       "   no_of_trainings_log  avg_training_score_log  length_of_service_log  \n",
       "0             0.000000                3.891820               2.079442  \n",
       "1             0.000000                4.094345               1.386294  \n",
       "2             0.000000                3.912023               1.945910  \n",
       "3             0.693147                3.912023               2.302585  \n",
       "4             0.000000                4.290459               0.693147  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfnact=pd.get_dummies(df[['department','region','education','gender','recruitment_channel']],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfnact=dfnact.join(df[['no_of_trainings_log', 'age_log', 'previous_year_rating', 'length_of_service_log',\n",
    "       'KPIs_met >80%', 'awards_won?', 'avg_training_score_log', 'is_promoted']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['department_Finance', 'department_HR', 'department_Legal',\n",
       "       'department_Operations', 'department_Procurement', 'department_R&D',\n",
       "       'department_Sales & Marketing', 'department_Technology',\n",
       "       'region_region_10', 'region_region_11', 'region_region_12',\n",
       "       'region_region_13', 'region_region_14', 'region_region_15',\n",
       "       'region_region_16', 'region_region_17', 'region_region_18',\n",
       "       'region_region_19', 'region_region_2', 'region_region_20',\n",
       "       'region_region_21', 'region_region_22', 'region_region_23',\n",
       "       'region_region_24', 'region_region_25', 'region_region_26',\n",
       "       'region_region_27', 'region_region_28', 'region_region_29',\n",
       "       'region_region_3', 'region_region_30', 'region_region_31',\n",
       "       'region_region_32', 'region_region_33', 'region_region_34',\n",
       "       'region_region_4', 'region_region_5', 'region_region_6',\n",
       "       'region_region_7', 'region_region_8', 'region_region_9',\n",
       "       'education_Below Secondary', 'education_Master's & above', 'gender_m',\n",
       "       'recruitment_channel_referred', 'recruitment_channel_sourcing',\n",
       "       'no_of_trainings_log', 'age_log', 'previous_year_rating',\n",
       "       'length_of_service_log', 'KPIs_met >80%', 'awards_won?',\n",
       "       'avg_training_score_log', 'is_promoted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfnact.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=dfnact.drop('is_promoted',axis=1)\n",
    "y=dfnact['is_promoted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain,xtest,ytrain,ytest=train_test_split(X.values,y.values,train_size=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Without any handling of missing values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=3, random_state=False, shuffle=False),\n",
       "             error_score='raise-deprecating',\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=100, multi_class='warn',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='warn',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid={'C': [100, 10, 1.0, 0.1, 0.01],\n",
       "                         'penalty': ['l1', 'l2']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='f1_macro', verbose=0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "from sklearn.model_selection import KFold\n",
    "import numpy as np\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "model=LogisticRegression()\n",
    "penalty = ['l1','l2']\n",
    "c_values = [100, 10, 1.0, 0.1, 0.01]\n",
    "grid={'C':c_values,'penalty':penalty}\n",
    "cv=KFold(n_splits=3,random_state=False,shuffle=False)\n",
    "\n",
    "clf=GridSearchCV(model,grid,cv=cv,n_jobs=-1,scoring='f1_macro')\n",
    "clf.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 100, 'penalty': 'l2'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD8CAYAAAC8TPVwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAZi0lEQVR4nO3de3xV1Zn/8c+TIBetFZD7pYhc7GA7QxUDY7WIyFUr4IUCU2W8DI6FTmtbp9p2Bsfa/vxVwXn5EsGAjFgVSkvR1BERowNYi4TbCIhIBIEQroKg9aeQk+f3x9mkp5jLySHJYW2/b1/rlXOevfbZ62heTx7XXntvc3dERCQMOdkegIiIpE9JW0QkIEraIiIBUdIWEQmIkraISECUtEVEAqKkLSJSBTObbWb7zGxDSuweM9tlZuuiNjxl291mVmxmm81sSEp8aBQrNrO7UuJdzewNM9tiZr8xs8Y1jUlJW0Skak8AQyuJP+TuvaP2AoCZ9QLGAOdH+zxqZrlmlgtMA4YBvYCxUV+A/xt9Vg/gEHBLTQNS0hYRqYK7LwMOptl9BDDP3T91921AMZAXtWJ33+ruR4F5wAgzM+By4HfR/nOAkTUdpFEtv0OtHTuwVZdcymc063Bptocgp6Cyo7vsZD+jNjmncetutwETUkL57p6fxq6TzOxGYBXwQ3c/BHQEVqT0KYliADtPiPcFzgY+cPeySvpXSZW2iHxuuXu+u/dJaekk7OlAN6A3sBuYEsUr+4PjGcSrVe+VtohIgypP1OvHu/ve46/NbCbwfPS2BOic0rUTUBq9rix+AGhuZo2iaju1f5VUaYtIvCTK0m8ZMLP2KW9HAcdXlhQAY8ysiZl1BXoAK4EioEe0UqQxyZOVBZ68W9+rwHXR/uOB52o6viptEYkV9/I6+ywzmwtcBrQysxJgMnCZmfUmOZXxHnBb8ri+0czmA28BZcBEd09EnzMJWAzkArPdfWN0iB8D88zsPmAt8HiNY6rvW7PqRKRURicipTJ1cSLyaMn69E9EdvrqSR+voanSFpF4qcNK+1SkpC0i8VLPJyKzTUlbROJFlbaISDg8w1UhoVDSFpF4KVelLSISDk2PiIgERCciRUQCokpbRCQgOhEpIhIQnYgUEQlHdLuP2FLSFpF40Zy2iEhAND0iIhIQVdoiIgFJHMv2COqVkraIxIumR0REAqLpERGRgKjSFhEJiJK2iEg4XCciRUQCojltEZGAaHpERCQgqrRFRAKiSltEJCCqtEVEAlKmhyCIiIRDlbaISEBiPqedk+0BiIjUKS9Pv9XAzGab2T4z25ASe8DM3jazN81soZk1j+LnmNn/M7N1UZuRss+FZrbezIrN7GEzsyje0syWmNmW6GeLmsakpC0i8VJenn6r2RPA0BNiS4CvuPvfAu8Ad6dse9fde0ftn1Pi04EJQI+oHf/Mu4BCd+8BFEbvq6WkLSLxUoeVtrsvAw6eEHvJ3Y+f7VwBdKruM8ysPfBFd/+TuzvwJDAy2jwCmBO9npMSr5KStojES1lZ2s3MJpjZqpQ2oZZHuxlYlPK+q5mtNbOlZnZpFOsIlKT0KYliAG3dfTdA9LNNTQfUiUgRiRf3WnT1fCA/k8OY2U+BMuDpKLQb+JK7v29mFwLPmtn5gFV26EyOCUraIhI3DbB6xMzGA1cBA6MpD9z9U+DT6PVqM3sX6Emysk6dQukElEav95pZe3ffHU2j7Kvp2JoeEZF4qdsTkZ9hZkOBHwNXu/vHKfHWZpYbvT6X5AnHrdG0x4dm1i9aNXIj8Fy0WwEwPno9PiVeJVXaIhIvdXhxjZnNBS4DWplZCTCZ5GqRJsCSaOXeimilyDeAe82sDEgA/+zux09i3k5yJUozknPgx+fB7wfmm9ktwA7g+prGpKQtIvGSSNTZR7n72ErCj1fRdwGwoIptq4CvVBJ/HxhYmzEpaYtIvMT8ikglbRGJFyVtEZGA6IZRIiLh8PKMl0AHQUlbROJF0yMiIgGpw9UjpyIlbRGJF1XaIiIBiXnS1mXsGfjZL6fyjSvHMPLbf7ld7rTHn+LyEd/m2vETuXb8RJa9vrJi28wnf8Ow0Tdz1Zhb+eMbqyvir61YxVVjbmXY6JuZ9ev5nznOL6c+ykVXjKrfLyMN6nv/8k/877pXWLe2kKd+PY0mTZqQ/9iDrF61hDWrl/Cbefmcccbp2R5m2NzTbwFS0s7AyOGDmDH1vs/Eb/jWSBbMmcaCOdP4xsV5ALy7bTuLCpfy3FMzmDH1Pn7+4CMkEgkSiQT3TZnG9Ck/p+Dpx3jh5f/h3W3bKz5rw6Z3OPLRnxvsO0n969ChHZMm3kzffsPp/bWB5Obm8q3RI/jhj+7hwj6DuODCQezcsYuJ37kp20MNWz3feyTbapweMbMvk7xRd0eStxMsBQrcfVM9j+2U1af3V9m1e29afV9ZvoJhA/vTuHFjOnVox5c6dWD9pncA+FKnDnTu2B6AYQP788ryFXTr2oVEIsGUaY/zq3t+TOGy1+vte0jDa9SoEc2aNeXYsWOc3qwZu3fv4cMPP6rY3rRZUzzQCvCUEfMlf9VW2mb2Y2AeyfvBrgSKotdzzazGx+J83sxd8AdG3Xg7P/vlVA4f+RCAffvfp13b1hV92rZpxb79B9i3/wDt2pwYfx+AZxb8gQGX9KN1q5YN+wWkXpWW7mHqQzPY9u5KSnas5fCRIyx5eRkAs2ZOZdfOdXz5vO48Mm12lkcauEQi/RagmqZHbgEucvf73f2pqN0P5EXbKpX6NIhZT86ty/Gesr416koWzZ/Ngiem0frsljzwyEwAvJJ7nRtW6XSaWTLJv/TqcsZdd3V9D1kaWPPmZ3H1N4fQvWc/One5gDPOOJ1x464B4NZ/+gGdu1zApre3MPp6/bc/GV5ennYLUU1JuxzoUEm8fbStUu6e7+593L3PrTdWdpOs+GnVsgW5ubnk5ORw3dXD2PBWcgqkbetW7Nm7v6Lf3n0HaN36bNq2acWefSfEW53Npi3vsqNkN8O/dTODrx3PJ598yrDRNzf495G6N3DgpWx7bwcHDhykrKyMhc8u4u/79anYXl5ezm9/W8A1o67M4ihjoNzTbwGqKWl/Hyg0s0Vmlh+1F0k+Nfh79T+8cOw/8JdnfxYufZ3u53YBYMAl/VhUuJSjR49SUrqHHSWlfPVvevKVL/dkR0kpJaV7OHbsGIsKlzLgkn70vziPpX94hpcWzOGlBXNo2rQJi+brf5fjYOeOXfTtewHNmjUF4PIBl/D221vo1u2cij5XXTmIzZuLszTCmKjDB/ueiqo9EenuL5pZT5LTIR1JzmeXAEXuHuaEUB24c/L9FK19kw8+OMLAkd/mO7fcQNHaN9m8ZSsYdGzXlsn/+i8AdD+3C0Muv5Sr/+E2GuXm8tMffIfc3FwAfnLH7dz2g5+RSCQYddXgikQv8bSyaC2///1/U7RyMWVlZaxbt5GZs57m5Zfmc+YXv4CZ8eabbzFx0t3ZHmrYAq2g02X1fab62IGt8f43KBlp1uHSmjvJ507Z0V2VPQS3Vv7872PSzjln3DvvpI/X0HRFpIjES6DTHulS0haReIn59IiStojESqhL+dKlpC0i8aJKW0QkIEraIiIBCfTy9HQpaYtIrOgZkSIiIVHSFhEJiFaPiIgEJOaVtp5cIyLxUod3+TOz2Wa2z8w2pMRamtkSM9sS/WwRxc3MHjazYjN708wuSNlnfNR/i5mNT4lfaGbro30eNrMaL6tX0haRWPFEedotDU8AQ0+I3QUUunsPknc8Pf5AmGFAj6hNAKZDMskDk4G+JG++N/l4oo/6TEjZ78RjfYaStojESx1W2u6+DDh4QngEMCd6PQcYmRJ/0pNWAM3NrD0wBFji7gfd/RCwBBgabfuiu//Jk3fuezLls6qkpC0iseLlnnZLfcpW1CakcYi27r4bIPrZJop3BHam9CuJYtXFSyqJV0snIkUkXmpxItLd84H8OjpyZfPRnkG8Wqq0RSReymvRMrM3mtog+rkvipcAnVP6dQJKa4h3qiReLSVtEYkVLytPu2WoADi+AmQ88FxK/MZoFUk/4HA0fbIYGGxmLaITkIOBxdG2D82sX7Rq5MaUz6qSpkdEJF7q8NoaM5sLXAa0MrMSkqtA7gfmm9ktwA7g+qj7C8BwoBj4GLgJwN0PmtnPgaKo373ufvzk5u0kV6g0AxZFrfox6XFjkg163JhUpi4eN3bo+svSzjktfvs/etyYiEhWxfsqdiVtEYkX3eVPRCQkqrRFRMLhZdkeQf1S0haRWHFV2iIiAVHSFhEJhyptEZGAKGmLiATEE8FdL1MrStoiEiuqtEVEAuLlqrRFRIKhSltEJCDuqrRFRIKhSltEJCDlWj0iIhIOnYgUEQmIkraISEDq+WFcWaekLSKxokpbRCQgWvInIhKQhFaPiIiEQ5W2iEhANKctIhIQrR4REQmIKm0RkYAkynOyPYR6paQtIrES9+mReP9JEpHPnXK3tFt1zOw8M1uX0o6Y2ffN7B4z25USH56yz91mVmxmm81sSEp8aBQrNrO7Tub7qdIWkVipqyV/7r4Z6A1gZrnALmAhcBPwkLs/mNrfzHoBY4DzgQ7Ay2bWM9o8DRgElABFZlbg7m9lMi4lbRGJlXqaHhkIvOvu282q/KMwApjn7p8C28ysGMiLthW7+1YAM5sX9T01k/aXul9V34eQADXKyc32ECSmapr2yNAYYG7K+0lmdiOwCvihux8COgIrUvqURDGAnSfE+2Y6EM1pi0isJMpz0m5mNsHMVqW0CSd+npk1Bq4GfhuFpgPdSE6d7AamHO9ayXC8mnhGND0iIrFSm2zo7vlAfg3dhgFr3H1vtM/e4xvMbCbwfPS2BOicsl8noDR6XVW81lRpi0is1NXqkRRjSZkaMbP2KdtGARui1wXAGDNrYmZdgR7ASqAI6GFmXaOqfUzUNyOqtEUkVuryhlFmdjrJVR+3pYR/ZWa9SRb17x3f5u4bzWw+yROMZcBEd09EnzMJWAzkArPdfWPGY/J6XonevnmvmC91l0wc+uSjbA9BTkGffLLjpDPu8nbXpZ1zLt3zu+CueVelLSKx4pWe94sPJW0RiZUy3U9bRCQcqrRFRAJSnu0B1DMlbRGJFVXaIiIBUaUtIhKQhCptEZFwxPxpY0raIhIv5aq0RUTCEfdLsJW0RSRWdCJSRCQg5VU/WSYWlLRFJFYS2R5APVPSFpFY0eoREZGAaPWIiEhAtHpERCQgmh4REQmIlvyJiAQkoUpbRCQcqrRFRAKipC0iEpCYPyJSSVtE4kWVtohIQHQZu4hIQLROW0QkIJoeEREJiJK2iEhA4n7vkZxsD0BEpC6VW/qtJmb2npmtN7N1ZrYqirU0syVmtiX62SKKm5k9bGbFZvammV2Q8jnjo/5bzGz8yXw/JW0RiZVELVqaBrh7b3fvE72/Cyh09x5AYfQeYBjQI2oTgOmQTPLAZKAvkAdMPp7oM6GkLSKxUo6n3TI0ApgTvZ4DjEyJP+lJK4DmZtYeGAIscfeD7n4IWAIMzfTgStoiEivltWhmNsHMVqW0CSd8nAMvmdnqlG1t3X03QPSzTRTvCOxM2bckilUVz4hORIpIrNSmfnb3fCC/mi5fd/dSM2sDLDGzt6vpW9ksuVcTz4gqbRGJldpU2jVx99Lo5z5gIck56b3RtAfRz31R9xKgc8runYDSauIZUdIWkVgpM0+7VcfMzjCzM4+/BgYDG4AC4PgKkPHAc9HrAuDGaBVJP+BwNH2yGBhsZi2iE5CDo1hGND0iIrFSh+u02wILzQySufIZd3/RzIqA+WZ2C7ADuD7q/wIwHCgGPgZuAnD3g2b2c6Ao6nevux/MdFBK2iISK3V1RaS7bwX+rpL4+8DASuIOTKzis2YDs+tiXEraIhIrJ7GULwhK2iISK/FO2UraIhIzumGUiEhAEjGvtZW0RSRWVGmLiATEVWmLiIQj7pW2rog8SVMfuY/1W5bz6uvPVcSaNz+LeQtn8cfVi5i3cBZnnfVFAK65/ioK/7iQwj8upGDx0/T6ynkV+wwYeAnLi/6b19e8yKTv39rg30PqR5MmTVi+vICVK19kzZqX+bd/+wEAM2b8ipUrX6SoaDHPPDODM844vWKfa6+9irVrC1mz5mXmzHk4W0MPVgPc5S+rlLRP0vxnFjLuur++MdikO27ltaUr+PqFw3ht6Qom3ZFMwju2l3DN8PEM/Poo/vOBGTzwn/8BQE5ODr988Gf8w3W30b/vNxl53XB6ntetwb+L1L1PP/2UoUPHkJc3lLy8oQwa1J+8vK9x5533kpc3lIsuGsLOnbu4/fZ/BKBbt3O4887vMGDANVxwwRX86Ef/kd0vECCvRQuRkvZJWvH6ag4dOvxXsSHDL2f+3GcBmD/3WYZembx4atXKdRw+fASA1UX/S/sObQH42oVf5b2tO9ixvYRjx47x3IJFDBl+eQN+C6lPf/7zxwCcdlojTjutEe7Ohx9+VLG9WbOmJC+mg5tvHsdjjz3JBx8kf6f273+/4QccuDI87RYiJe160LrN2ezbewCAfXsP0Kp1y8/0GXvDtbzy8nIA2rVvy65deyq27S7dQ7v2bT6zj4QpJyeHN95YxM6dayksfI2ionUA5Oc/yPbtqznvvG48+uh/AdCjR1e6dz+XV1/9PUuXPsugQf2zOfQgeS3+CVHGSdvMbqpmW8WNxT8+eijTQ8TWxZfmMe6Ga/jF5CkARDek+Sth/jpJZcrLy+nbdxjduvXloov+jl69egIwYcKP6Nr1It5+u5jrr/8mAI0aNaJ793MYNGg048d/l+nTf1VxTkTSU5e3Zj0VnUylXeVkm7vnu3sfd+9zeuOMH4UWrP373qdN21YAtGnbigP7/3JDr785vydTHr6Xfxw3qWJaZXfpHjp2bFfRp32HduzdvQ+Jl8OHj7Bs2QoGD76sIlZeXs7vfvcHRo4cDsCuXbt5/vmXKCsr4733drJly1a6dz8nOwMO1Oe60o6eKFxZW0/ytoVSiZcWvcroscnHxo0eO5LFL7wCQMdO7Xn81w/z3dvuYuu72yv6r1uzga7dutC5S0dOO+00Rlw7jMWLXs3K2KVutWrVsqJSbtq0CZdffgnvvLOVc8/tUtFn+PAr2Ly5GICCgsX0738xAGef3YIePbqybduOhh94wOJeade0TrstyYdSnjjHYcDr9TKiwDw66wEuviSPlmc3Z/XGV3jw/kd45KGZPPbEQ4y94Vp2lexmwvg7ALjjX2+nRcuz+D9T/h2ARFkZQweMJpFI8JM7f8HcBTPJzc1h3lMLeeft4mx+Lakj7dq1YdasqeTm5pKTk8OCBc+zaFEhr7yygDPP/AJmxvr1b/Hd7/4UgCVLlnLFFd9g7dpCEokEd9/9Cw4e/CDL3yIsCQ+zgk6XeTVf0MweB/7L3V+rZNsz7j6upgO0b94r3v8GJSOHPvmo5k7yufPJJzsqe55irYzrMirtnPPM9oUnfbyGVm2l7e63VLOtxoQtItLQQp2rTpcuYxeRWAl1rjpdStoiEiuhXp6eLiVtEYkVTY+IiAQk7qtHlLRFJFY0PSIiEhCdiBQRCYjmtEVEAqLpERGRgFR3lXccKGmLSKwkYl5p6yEIIhIrdfWMSDPrbGavmtkmM9toZt+L4veY2S4zWxe14Sn73G1mxWa22cyGpMSHRrFiM7vrZL6fKm0RiZU6nB4pA37o7mvM7ExgtZktibY95O4PpnY2s17AGOB8oAPwspn1jDZPAwYBJUCRmRW4+1uZDEpJW0Ripa5ORLr7bmB39PpDM9sEdKxmlxHAPHf/FNhmZsVAXrSt2N23ApjZvKhvRklb0yMiEiv18eQaMzsH+BrwRhSaFD0QZraZHX88V0dgZ8puJVGsqnhGlLRFJFYS7mm31OfZRm3CiZ9nZl8AFgDfd/cjwHSgG9CbZCU+5XjXSobj1cQzoukREYmV2kyPuHs+kF/VdjM7jWTCftrdfx/tszdl+0zg+ehtCdA5ZfdOQGn0uqp4ranSFpFYqcPVIwY8Dmxy96kp8fYp3UYBG6LXBcAYM2tiZl2BHsBKoAjoYWZdzawxyZOVBZl+P1XaIhIrdbh65OvADcB6M1sXxX4CjDWz3iSnON4DbouOu9HM5pM8wVgGTHT3BICZTQIWA7nAbHffmOmgqn1GZF3QMyKlMnpGpFSmLp4Rmdehf9o5Z2Xp0ng9I1JEJDS6YZSISEASHu+bsyppi0is6IZRIiIB0a1ZRUQCojltEZGAlGt6REQkHKq0RUQCotUjIiIB0fSIiEhAND0iIhIQVdoiIgFRpS0iEpBE8sZ6saWkLSKxosvYRUQCosvYRUQCokpbRCQgWj0iIhIQrR4REQmILmMXEQmI5rRFRAKiOW0RkYCo0hYRCYjWaYuIBESVtohIQLR6REQkIDoRKSISEE2PiIgERFdEiogERJW2iEhA4j6nbXH/q3QqMbMJ7p6f7XHIqUW/F1IbOdkewOfMhGwPQE5J+r2QtClpi4gERElbRCQgStoNS/OWUhn9XkjadCJSRCQgqrRFRAKipC0iEhAl7QZiZkPNbLOZFZvZXdkej2Sfmc02s31mtiHbY5FwKGk3ADPLBaYBw4BewFgz65XdUckp4AlgaLYHIWFR0m4YeUCxu29196PAPGBElsckWebuy4CD2R6HhEVJu2F0BHamvC+JYiIitaKk3TCskpjWWopIrSlpN4wSoHPK+05AaZbGIiIBU9JuGEVADzPramaNgTFAQZbHJCIBUtJuAO5eBkwCFgObgPnuvjG7o5JsM7O5wJ+A88ysxMxuyfaY5NSny9hFRAKiSltEJCBK2iIiAVHSFhEJiJK2iEhAlLRFRAKipC0iEhAlbRGRgPx/dRFFOhj1aqcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9329197834944961\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.96     15087\n",
      "           1       0.80      0.25      0.38      1356\n",
      "\n",
      "    accuracy                           0.93     16443\n",
      "   macro avg       0.87      0.62      0.67     16443\n",
      "weighted avg       0.93      0.93      0.92     16443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "y_pred=clf.predict(xtest)\n",
    "sns.heatmap(confusion_matrix(ytest,y_pred),annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "print(accuracy_score(ytest,y_pred))\n",
    "print(classification_report(ytest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise-deprecating',\n",
       "                   estimator=DecisionTreeClassifier(class_weight=None,\n",
       "                                                    criterion='gini',\n",
       "                                                    max_depth=None,\n",
       "                                                    max_features=None,\n",
       "                                                    max_leaf_nodes=None,\n",
       "                                                    min_impurity_decrease=0.0,\n",
       "                                                    min_impurity_split=None,\n",
       "                                                    min_samples_leaf=1,\n",
       "                                                    min_samples_split=2,\n",
       "                                                    min_weight_fraction_leaf=0.0,\n",
       "                                                    presort=False,\n",
       "                                                    random_state=None,\n",
       "                                                    splitter='best'),\n",
       "                   iid='w...\n",
       "                   param_distributions={'criterion': ['gini', 'entropy'],\n",
       "                                        'max_depth': [3, None],\n",
       "                                        'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001D7D697BEC8>,\n",
       "                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001D7D69F7088>},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "param_dist = {\"max_depth\": [3, None],\n",
    "              \"max_features\": randint(1, 9),\n",
    "              \"min_samples_leaf\": randint(1, 9),\n",
    "              \"criterion\": [\"gini\", \"entropy\"]}\n",
    "\n",
    "tree = DecisionTreeClassifier()\n",
    "tree_cv = RandomizedSearchCV(tree, param_dist, cv=3)\n",
    "tree_cv.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 8,\n",
       " 'min_samples_leaf': 7}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9299397920087575\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.96     15087\n",
      "           1       0.73      0.24      0.36      1356\n",
      "\n",
      "    accuracy                           0.93     16443\n",
      "   macro avg       0.83      0.62      0.66     16443\n",
      "weighted avg       0.92      0.93      0.91     16443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "y_pred=tree_cv.predict(xtest)\n",
    "sns.heatmap(confusion_matrix(ytest,y_pred),annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "print(accuracy_score(ytest,y_pred))\n",
    "print(classification_report(ytest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 320 candidates, totalling 960 fits\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   1.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   2.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   2.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   3.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   2.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.7s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   2.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.9s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   1.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   1.0s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.2s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.5s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.6s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.4s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   2.1s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.8s\n",
      "[CV] bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=True, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   1.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   2.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.6s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   2.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   2.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   2.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   2.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   2.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   2.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   2.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   2.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   2.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   2.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   1.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   2.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   2.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   2.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   2.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   2.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   2.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   2.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.6s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   2.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   2.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   2.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   2.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   2.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=33, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=41, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=48, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=56, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=64, total=   1.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   1.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=72, total=   2.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   2.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   2.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80, total=   2.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=25, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=33, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=41, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=48, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=56, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=64, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=72, total=   1.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   2.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   2.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80, total=   2.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=25, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=33, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=41, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   1.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=48, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=56, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=64, total=   1.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   1.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   2.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=72, total=   2.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   2.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   1.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80, total=   2.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   0.2s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=17, total=   0.4s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=25, total=   0.6s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=33, total=   0.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   0.9s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=41, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   1.0s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=48, total=   1.1s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=56, total=   1.3s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.7s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.5s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=64, total=   1.6s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.6s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.6s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=72, total=   1.6s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.6s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   1.8s\n",
      "[CV] bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80 \n",
      "[CV]  bootstrap=False, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=80, total=   2.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 960 out of 960 | elapsed: 14.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators='warn', n_jobs=None,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'bootstrap': [True, False], 'max_depth': [2, 4],\n",
       "                         'max_features': ['auto', 'sqrt'],\n",
       "                         'min_samples_leaf': [1, 2],\n",
       "                         'min_samples_split': [2, 5],\n",
       "                         'n_estimators': [10, 17, 25, 33, 41, 48, 56, 64, 72,\n",
       "                                          80]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=2)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 10, stop = 80, num = 10)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [2,4]\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "param_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "clfr = RandomForestClassifier()\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "rf_grid = GridSearchCV(estimator = clfr, param_grid = param_grid, cv = 3, verbose=2)\n",
    "rf_grid.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'max_depth': 4,\n",
       " 'max_features': 'auto',\n",
       " 'min_samples_leaf': 2,\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 17}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9245879705649821\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      1.00      0.96     15087\n",
      "           1       0.95      0.09      0.17      1356\n",
      "\n",
      "    accuracy                           0.92     16443\n",
      "   macro avg       0.94      0.55      0.56     16443\n",
      "weighted avg       0.93      0.92      0.89     16443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "y_pred=rf_grid.predict(xtest)\n",
    "sns.heatmap(confusion_matrix(ytest,y_pred),annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "print(accuracy_score(ytest,y_pred))\n",
    "print(classification_report(ytest,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 405 candidates, totalling 1215 fits\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   1.9s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.4s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.4s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.0s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.1s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.6s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   4.1s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   4.1s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.2s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   5.1s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.8s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.4s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.7s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.2s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.4s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.4s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6, total=   5.0s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.9s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0, total=   4.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0, total=   5.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0, total=   1.9s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0, total=   1.9s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6, total=   3.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.0s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.0s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.0s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.0s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   5.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.0s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0, total=   2.4s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8, total=   5.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0, total=   4.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0, total=   4.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8, total=   5.0s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.1s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.4s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.0s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.3s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.5s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.1s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.2s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0, total=   2.7s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0, total=   2.6s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0, total=   2.8s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.3s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.4s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.6, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.8s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.3s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.3s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.7s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.3s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.3s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.3s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.3s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   2.9s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   4.3s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.1s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.3s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.3s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.7s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.3s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.2s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.3s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.1s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.9s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.1s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.2s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.7s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.8s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   5.6s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.2s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.2s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   5.8s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.9s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.9s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6, total=   5.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8, total=   4.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.6s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.6s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.6s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.9s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8, total=   7.1s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6, total=   5.1s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.9s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.9s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.8s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.8s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   3.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   4.1s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   5.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   7.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   6.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   5.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   6.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   5.3s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   5.1s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   4.1s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.9s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.3s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6, total=   4.2s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.7s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.9s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.3s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.3s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.3s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.3s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.3s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6, total=   2.3s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8, total=   2.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.3s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.8s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0, total=   6.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6, total=   3.2s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8, total=   7.2s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0, total=   5.7s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0, total=   4.8s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0, total=   5.7s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6, total=   6.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6, total=   4.1s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.9s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.7s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.1s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.1s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0, total=   3.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.4s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=0.8, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0, total=   3.4s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.8s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.8s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.8s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   2.8s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.8s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   2.8s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   5.1s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   4.8s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.9s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   6.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   4.1s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   4.2s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   5.3s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8, total=  11.0s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   5.6s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   5.1s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   4.3s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   4.1s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.3s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.1s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   4.2s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   4.3s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   4.2s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.9s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   4.3s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   4.4s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   4.4s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   5.2s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.8s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.9s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   5.0s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.9s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.9s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   5.1s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.8s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   5.2s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   5.0s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   5.0s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   5.0s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=5, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6, total=   4.2s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6, total=   6.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8, total=   4.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0, total=   5.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0, total=   5.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0, total=   4.2s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6, total=   4.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=0.6, total=   4.2s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8, total=   5.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=0.8, total=   4.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0, total=   4.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=1, subsample=1.0, total=   5.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6, total=   4.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6, total=   5.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=0.6, total=   5.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0, total=   4.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0, total=   5.2s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=1.0, total=   4.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6, total=   4.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=0.6, total=   5.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8, total=   4.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=0.8, total=   4.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0, total=   4.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0, total=   4.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=10, subsample=1.0, total=   4.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6, total=   5.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6, total=   5.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8, total=   5.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8, total=   6.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=0.8, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0, total=   5.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0, total=   5.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=1, subsample=1.0, total=   5.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6, total=   5.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6, total=   5.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=0.6, total=   5.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8, total=   6.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0, total=   6.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=5, subsample=1.0, total=   6.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6, total=   6.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6, total=   7.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=0.6, total=   5.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8, total=   6.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8, total=   7.2s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=0.8, total=   8.2s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0, total=   5.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0, total=   5.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=5, min_child_weight=10, subsample=1.0, total=   5.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.6, total=   4.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   4.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   4.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.2s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   4.2s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=0.8, total=   4.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   4.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=5, subsample=1.0, total=   4.2s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   8.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   5.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.6, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   4.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=0.8, total=   4.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   4.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=3, min_child_weight=10, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   5.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   5.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   5.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=1, subsample=1.0, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.6, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=5, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   5.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   4.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=0.8, total=   4.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=4, min_child_weight=10, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.6, total=   5.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   5.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=0.8, total=   5.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   5.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   5.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=1, subsample=1.0, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   5.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.6, total=   5.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=0.8, total=   5.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   5.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=5, subsample=1.0, total=   5.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   5.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.6, total=   5.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   5.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=0.8, total=   5.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   5.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1.5, max_depth=5, min_child_weight=10, subsample=1.0, total=   5.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0, total=   3.4s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=5, subsample=1.0, total=   3.4s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=3, min_child_weight=10, subsample=1.0, total=   2.9s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6, total=  12.7s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=0.6, total=   4.3s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.4s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.7s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=0.8, total=   5.1s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0, total=   7.2s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0, total=   5.4s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=4, min_child_weight=10, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6, total=   5.2s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6, total=   5.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=0.6, total=   6.2s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8, total=   5.2s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8, total=   8.9s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=0.8, total=   8.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0, total=   5.9s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.4s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=2, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6, total=   2.9s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=0.8, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.2s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=1, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=0.6, total=   3.1s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8, total=   4.2s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=0.8, total=   5.2s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0, total=   7.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=5, subsample=1.0, total=   3.3s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6, total=   4.1s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=0.6, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.3s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=0.8, total=   3.4s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.6s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=3, min_child_weight=10, subsample=1.0, total=   3.2s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6, total=   4.1s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=0.6, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8, total=   4.1s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8, total=   3.9s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.9s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=1, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=0.6, total=   3.9s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=5, subsample=1.0, total=   3.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6, total=   4.1s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=0.6, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8, total=   3.9s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=0.8, total=   4.0s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=4, min_child_weight=10, subsample=1.0, total=   3.8s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6, total=   5.1s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.8s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=0.6, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=1, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=0.8, total=   4.8s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=5, subsample=1.0, total=   4.5s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=0.6, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=0.8, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.6s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.7s\n",
      "[CV] colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=5, max_depth=5, min_child_weight=10, subsample=1.0, total=   4.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 1215 out of 1215 | elapsed: 75.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, gamma=None,\n",
       "                                     gpu_id=None, importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None, max_delta_step=None,\n",
       "                                     max_depth=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constrai...\n",
       "                                     reg_lambda=None, scale_pos_weight=None,\n",
       "                                     subsample=None, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=None),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'colsample_bytree': [0.6, 0.8, 1.0],\n",
       "                         'gamma': [0.5, 1, 1.5, 2, 5], 'max_depth': [3, 4, 5],\n",
       "                         'min_child_weight': [1, 5, 10],\n",
       "                         'subsample': [0.6, 0.8, 1.0]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "model = XGBClassifier()\n",
    "params = {\n",
    "        'min_child_weight': [1, 5, 10],\n",
    "        'gamma': [0.5, 1, 1.5, 2, 5],\n",
    "        'subsample': [0.6, 0.8, 1.0],\n",
    "        'colsample_bytree': [0.6, 0.8, 1.0],\n",
    "        'max_depth': [3, 4, 5]\n",
    "        }\n",
    "xgb_grid = GridSearchCV(estimator = model, param_grid = params, cv = 3, verbose=2)\n",
    "xgb_grid.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 0.8,\n",
       " 'gamma': 1.5,\n",
       " 'max_depth': 4,\n",
       " 'min_child_weight': 1,\n",
       " 'subsample': 1.0}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9441707717569786\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      1.00      0.97     15065\n",
      "           1       0.93      0.36      0.52      1378\n",
      "\n",
      "    accuracy                           0.94     16443\n",
      "   macro avg       0.94      0.68      0.74     16443\n",
      "weighted avg       0.94      0.94      0.93     16443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "y_pred=xgb_grid.predict(xtest)\n",
    "sns.heatmap(confusion_matrix(ytest,y_pred),annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "print(accuracy_score(ytest,y_pred))\n",
    "print(classification_report(ytest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clearly we can see that XGBOOST is working efficiently than other ML model since ensemble model works much better in imbalenced dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To handle imbalenced dataset we have 2 option based on current scenareo\n",
    "<p><ul>OVER SAMPLING</ul></p>\n",
    "<p><ul>SMORTING</ul></p>     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of classes before fit Counter({0: 35157, 1: 3208})\n",
      "The number of classes after fit Counter({0: 35157, 1: 26367})\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luvdh\\AppData\\Roaming\\Python\\Python37\\site-packages\\imblearn\\utils\\_validation.py:591: FutureWarning: Pass sampling_strategy=0.75 as keyword args. From version 0.9 passing these as positional arguments will result in an error\n",
      "  FutureWarning,\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from collections import Counter\n",
    "\n",
    "os=RandomOverSampler(0.75)\n",
    "X_train_os,y_train_os=os.fit_resample(xtrain,ytrain)\n",
    "print(\"The number of classes before fit {}\".format(Counter(ytrain)))\n",
    "print(\"The number of classes after fit {}\".format(Counter(y_train_os)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oversapmling - Logistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7823389892355409\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.79      0.87     14996\n",
      "           1       0.25      0.72      0.37      1447\n",
      "\n",
      "    accuracy                           0.78     16443\n",
      "   macro avg       0.61      0.75      0.62     16443\n",
      "weighted avg       0.90      0.78      0.82     16443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logistic=LogisticRegression(C= 100, penalty='l2')\n",
    "logistic.fit(X_train_os,y_train_os)\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "y_pred=logistic.predict(xtest)\n",
    "sns.heatmap(confusion_matrix(ytest,y_pred),annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "print(accuracy_score(ytest,y_pred))\n",
    "print(classification_report(ytest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oversapling - Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD5CAYAAADsgWTDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYeklEQVR4nO3deXyU9bXH8c8hiERkVaBsVbBxF7EgaHFHZVFRaumFLiJgo6BUWq+C0IoX6xWtelt7K9cIVKgKaluW24II3CpqCRCVgmCRgFgiCLIoKiomOfePeUgHyDIJSYbf4/ft6/fKzHmW+Y3Gw+E8v5nH3B0REQlDnXRPQEREUqekLSISECVtEZGAKGmLiARESVtEJCBK2iIiAalb0y/w5fYNWlMoB+necXC6pyCHoWWbX7JDPUdlcs4Rx3Yo9/XMbApwJbDN3U+PYr8ErgL2AuuBwe7+oZkdD7wFrI0Oz3X3m6JjOgNPAJnAXOBWd3czawY8AxwPbAS+6+67ypuTKm0RkbI9AfQ6ILYAON3dOwJvA3cmbVvv7p2icVNSfCKQDWRFY985RwOL3D0LWBQ9L5eStojES3FR6qMC7r4Y2HlA7AV3L4ye5gJtyzuHmbUCGrn7Ek98mnEacE20+WpgavR4alK8TEraIhIvRYWpj0M3BJiX9Ly9mb1hZi+Z2flRrA1QkLRPQRQDaOnuWwCiny0qesEa72mLiNQm9+KU9zWzbBJti31y3D0nxWPHAoXAU1FoC/B1d98R9bBnmdlpQGl98ypf61PSFpF4KU49aUcJOqUknczMBpG4QNkjanng7l8AX0SPXzOz9cCJJCrr5BZKW2Bz9HirmbVy9y1RG2VbRa+t9oiIxIsXpz6qwMx6AaOAvu6+Jyne3MwyoscdSFxw3BC1PT42s3PMzIDrgNnRYXOAQdHjQUnxMqnSFpF4SeECY6rMbDpwEXCsmRUA40isFjkSWJDIwSVL+y4AxptZIVAE3OTu+y5iDuNfS/7m8a8++ATgWTMbCvwT6F/RnJS0RSReqlhBl3oq94GlhCeXse8fgT+WsS0POL2U+A6gR2XmpKQtIrHi1bMq5LClpC0i8VKJC5EhUtIWkXipxvbI4UhJW0TipRovRB6OlLRFJF5UaYuIBEQXIkVEAqILkSIi4XBXT1tEJBzqaYuIBETtERGRgKjSFhEJSNGX6Z5BjVLSFpF4UXtERCQgao+IiARElbaISECUtEVEwuG6ECkiEhD1tEVEAqL2iIhIQFRpi4gERJW2iEhAVGmLiASkUDdBEBEJhyptEZGAqKctIhIQVdoiIgFRpS0iEhBV2iIiAdHqERGRgLinewY1SklbROJFPW0RkYAoaYuIBEQXIkVEAlJUlO4Z1CglbRGJF7VHREQCEvOkXSfdExARqVZenPqogJlNMbNtZvZmUqyZmS0ws3XRz6ZR3MzsETPLN7OVZvbNpGMGRfuvM7NBSfHOZrYqOuYRM7OK5qSkLSKx4sWe8kjBE0CvA2KjgUXungUsip4D9AayopENTIREkgfGAd2ArsC4fYk+2ic76bgDX+sgStoiEi/FxamPCrj7YmDnAeGrganR46nANUnxaZ6QCzQxs1ZAT2CBu+90913AAqBXtK2Ruy9xdwemJZ2rTOppi0i8VGL1iJllk6h098lx95wKDmvp7lsA3H2LmbWI4m2ATUn7FUSx8uIFpcTLpaQtIvFSiQuRUYKuKEmnqrR+tFchXi4l7Ur62X8+zOJXl9GsaRNmPfk/ADz435N46dWl1D2iLu3atOIXY35Ko4ZHA7A2/x3GP/AIn3y6hzp16jBj0q8pLCzkuuG3l5xz6wfbufLyixk98iZm/WUBDz06iRbHHgvAwGuv4jt9K2xzyWGkRevm3P3rsRzTohleXMzMJ/+XZyb/kRtvH8IFPc/DvZid2z9k/Mj72L51Bz37Xcp1N38PgM/2fMb9ox9m3Zr1APzs4VGcd+m57Nq+i4GXDE7n2wpHza8e2WpmraIquxWwLYoXAO2S9msLbI7iFx0QfzGKty1l/3KZ1/CXq3y5fUOsvr0lb8UqjsrMZMw9D5Yk7VeXvka3zp2oWzeDhx+dDMBPhw+lsLCI/kNu4b6f387JWR348KPdNDy6ARkZGfud87tDRnDHj7Pp0ukMZv1lAav/sY6xtw2v9fdWm7p3jG8COqZFM45teQxrV63jqAaZTHv+cW4fMpZtWz7g00/2APDdodfSIes4Jox+mDO6nMbGde/y8UefcO7F3fjRbdcz5MphAJzVrSN79nzG3b8e85VI2ss2v1Th6omK7PnVjSnnnKNGPlbh65nZ8cCf3f306PkvgR3uPsHMRgPN3P0OM7sCuAXoQ+Ki4yPu3jW6EPkasG81yetAZ3ffaWbLgRHAUmAu8Bt3n1vefHQhspK6dDqDxo0a7hfr3q0zdesmEnHH005m67btAPxt2WuceEJ7Ts7qAECTxo0OStjvbnqPHbs+pPOZp9fC7KU27Ni2k7Wr1gGw59PPeCf/XZq3al6SsAEyM+uXfBndqrzVfPzRJwC8+fpqWrRqXrLfG0tXsnvXx7U3+TioxguRZjYdWAKcZGYFZjYUmABcZmbrgMui55BIuhuAfOBxYDiAu+8E7gGWR2N8FAMYBkyKjlkPzKtoThW2R8zsZBJXRduQ6LdsBua4+1sVvuOvoJl/eYFePS4EEgnZzMj+yVh2ffgRvS+9kCHf77/f/nMXvEivHheQvDxzwUuvkPf3VRzfrg13/PhGWrVsjoSpVduvcdLpWax+fQ0Aw0bdQJ/+Pflk9ycM+87Ig/bvO/AKlvx1aW1PM15SW8qXEncfWMamHqXs68DNZZxnCjCllHgeUKmKrdxK28xGATNINMyXkfhTwoDp0V8LJMljU6eTkZHBlZdfDEBhURFvrFzN/ePuYNrEB1n00t/IzXtjv2PmLXqJPpdeVPL8ovO68cIfnmDmtImc0+Usxv7iodp8C1KNMo/KZMKk8Tx8129KquyJ90/iqi79ef5PC+k/5Nv77d/5W2fRd+AV/Pe9j6VjuvFRVJT6CFBF7ZGhwNnuPsHdn4zGBBILxIeWdZCZZZtZnpnlTZo2vTrne9iaPXcBi19dxv3j7iipmlu2OJYunc6gaZPGZNavz/nnns2atetLjvnHug0UFRVz2slZJbEmjRtRr149AL7Ttxdr1q6r3Tci1SKjbgb3TxrP/D8t5MV5Lx+0ff7MhVzS54KS5984pQNjH7yd2weP4aNdu2tzqrHjxcUpjxBVlLSLgdalxFtF20rl7jnu3sXdu9xwXVl/u4iPV3LzmPzUc/zm/nFk1q9fEu/etTNvr3+Hzz7/nMLCIvJWrOKE9l8v2T5v4Yv0vvTC/c71wfZ/reP/6yu5dDiuHRKenz80infWvcvTOc+WxNq1/9cS3At6dmdj/j8BaNmmBfdPuodxP76Xf24oOOhcUknFnvoIUEU97ZHAoqjhvm9x+NeBb5C4SvqVc/u4CSx/YyUffribHtf8gOFDf8ik3z/D3i+/5EcjxwKJi5Hj7hhB40YNuW7Atxkw9FbMjPPPPZsLv9W15Fzz/+9lHn1w/H7nf/K52bz4Si4ZdTNo3LAhv/jZbbX6/uTQndn1DPr078m6Net5csEkAB6973H6DryC405oR3Gx8/57W5kwKtH6uuEng2jctDGj7vsJAEWFRQzqfSMA9zx6F53P7USTZo3537znePyh3zFnermLCyTm36dd4ZI/M6tDoh3ShkQ/uwBY7u4pNYTituRPqkecl/xJ1VXHkr9Px38/5ZzT4K6nDvn1aluFq0fcvRjIrYW5iIgcusIwLzCmSp+IFJF4iXl7RElbROIl0AuMqVLSFpFYCXUpX6qUtEUkXlRpi4gERElbRCQggX48PVVK2iISKyne+zFYStoiEi9K2iIiAdHqERGRgKjSFhEJiJK2iEg4vEjtERGRcKjSFhEJh5b8iYiERElbRCQg8W5pK2mLSLx4YbyztpK2iMRLvHO2kraIxIsuRIqIhESVtohIOFRpi4iERJW2iEg4vDDdM6hZStoiEiuuSltEJCBK2iIi4VClLSISECVtEZGAeJGlewo1SklbRGJFlbaISEC8ON6Vdp10T0BEpDp5ceqjPGZ2kpmtSBq7zWykmd1tZu8lxfskHXOnmeWb2Voz65kU7xXF8s1s9KG8P1XaIhIr7tVTabv7WqATgJllAO8BM4HBwH+5+4PJ+5vZqcAA4DSgNbDQzE6MNv8WuAwoAJab2Rx3X1OVeSlpi0is1FBPuwew3t3fNSvzD4WrgRnu/gXwjpnlA12jbfnuvgHAzGZE+1Ypaas9IiKxUlxkKQ8zyzazvKSRXcZpBwDTk57fYmYrzWyKmTWNYm2ATUn7FESxsuJVoqQtIrHixZb6cM9x9y5JI+fA85lZPaAv8FwUmgicQKJ1sgV4aN+upU2nnHiVqD0iIrFSA6tHegOvu/tWgH0/AczsceDP0dMCoF3ScW2BzdHjsuKVpkpbRGLFPfWRooEktUbMrFXStn7Am9HjOcAAMzvSzNoDWcAyYDmQZWbto6p9QLRvlajSFpFYqc5K28yOIrHq48ak8ANm1olEi2Pjvm3uvtrMniVxgbEQuNndi6Lz3ALMBzKAKe6+uqpzUtIWkVipriV/iXP5HuCYA2I/LGf/e4F7S4nPBeZWx5yUtEUkVor03SMiIuGozkr7cKSkLSKxEvfvHlHSFpFYqcSqkCApaYtIrKjSFhEJSFFxvD9+oqQtIrGi9oiISECKtXpERCQcWvInIhIQtUcOUWbr82v6JSRAR2SoXpCaofaIiEhAtHpERCQgMe+OKGmLSLyoPSIiEhCtHhERCUjN3Iz98KGkLSKx4qXeRzc+lLRFJFYK1R4REQmHKm0RkYCopy0iEhBV2iIiAVGlLSISkCJV2iIi4Yj53caUtEUkXopVaYuIhENfGCUiEhBdiBQRCUixqT0iIhKMonRPoIYpaYtIrGj1iIhIQLR6REQkIFo9IiISELVHREQCoiV/IiIBKVKlLSISjrhX2nXSPQERkepUXIlRETPbaGarzGyFmeVFsWZmtsDM1kU/m0ZxM7NHzCzfzFaa2TeTzjMo2n+dmQ06lPenpC0iseKW+kjRxe7eyd27RM9HA4vcPQtYFD0H6A1kRSMbmAiJJA+MA7oBXYFx+xJ9VShpi0isVGelXYarganR46nANUnxaZ6QCzQxs1ZAT2CBu+90913AAqBXVV9cSVtEYqWoEsPMss0sL2lkH3A6B14ws9eStrV09y0A0c8WUbwNsCnp2IIoVla8SnQhUkRipTLrtN09B8gpZ5fu7r7ZzFoAC8zsH+XsW9oreznxKlGlLSKxUp3tEXffHP3cBswk0ZPeGrU9iH5ui3YvANolHd4W2FxOvEqUtEUkVqoraZtZAzNruO8xcDnwJjAH2LcCZBAwO3o8B7guWkVyDvBR1D6ZD1xuZk2jC5CXR7EqUXtERGKlGr97pCUw0xLfz10XeNrdnzez5cCzZjYU+CfQP9p/LtAHyAf2AIMB3H2nmd0DLI/2G+/uO6s6KSVtEYmV6vruEXffAJxZSnwH0KOUuAM3l3GuKcCU6piXkraIxIpugiAiEpDimH85q5K2iMRK3L97RElbRGIl3nW2kraIxIwqbRGRgBRavGttJW0RiZV4p2wlbRGJGbVHREQCoiV/IiIBiXfKVtIWkZhRe0REJCBFMa+1lbRFJFZUaYuIBMRVaYuIhCPulbbuXFON8t/O5Y3XF5K3/AVyl8wFoGPHU3ll8RzeeH0hs2Y+QcOGRwNwaY/zWZo7jzdeX8jS3HlcfFH3dE5datCIEUN57bUF5OW9wNSpj3DkkUeSk/Mgb731Crm5c8nNnUvHjqcCcP755/D++6tK4nfe+eM0zz48xXjKI0SqtKvZpZf1Z8eOXSXPH/ufXzJq1D0sfjmX6wf9G/9+2zDG3f1Ltu/YyTX9rmfLlq2cdtpJzP3zUxzXvksaZy41oXXrlgwfPpizzurB559/wZNP/pb+/a8CYMyY/2TmzLkHHfPqq8u59tohtT3V2AgzFadOlXYNO+nEE1j8ci4ACxe9TL9+fQBYsWI1W7ZsBWD16rXUr1+fevXqpW2eUnPq1s0gM7M+GRkZZGZmlvx3l5pRiKc8QqSkXY3cnXlzp7M0dx43DP0+kEjIV111OQDfufZK2rVtfdBx3/72FaxY8SZ79+6t1flKzdu8eSu/+lUOb7+9hHfeWc7u3R+zaNHLANx997+zbNnzPPDAz/f7A7tbt2+ydOk8Zs2ayimnZKVr6sHySvwToionbTMbXM62bDPLM7O84uJPq/oSwbngomvo2q0XV171A4YNu57zz+vGDdk/ZfhN17M0dx4NGzZg794v9zvm1FNP5L57xzDs5lFpmrXUpCZNGnHllZdzyinn0aFDVxo0yGTAgH7cddcDnHnmJZx3Xl+aNm3CbbfdBMCKFW9y0knfolu33kyc+ATPPvt4mt9BeKrrbuyHq0OptP+jrA3unuPuXdy9S506DQ7hJcKy76+9H3ywg9mz53H22Z1Yu3Y9va/4Ht3O6c2MZ2azYcPGkv3btGnFH56bzOAht7Jhw7tpmrXUpEsuOY+NGzexfftOCgsLmTXrec45pzPvv78NgL179zJt2nN06dIJgI8//oRPP90DwPz5f+WII+pyzDFN0zb/EH2lK20zW1nGWEXi9vISOeqoTI4+ukHJ48suvZDVq9fSvPkxAJgZY+68lcdyfg9A48aNmDN7GmN/dh9/W5KXtnlLzdq0aTNdu55FZmZ9AC6+uDtr1+bzta+1KNmnb9/LWbNmLQAtWzYviXfpciZ16tTZ78K2VCzulXZFq0daAj2BA39rDPhbjcwoUC1bNucPz00GEheeZsyYxfwXXmTELUMZNux6AGbNmssTU58B4Obhg/nGCcczdsxIxo4ZCUDvPgP54IMdaZm/1Izly1cwc+Zcliz5C4WFRfz976uZPPlpZs+eyrHHNsPMWLlyDSNGjAGgX78+/OhHP6CwsJDPP/+c664bkeZ3EJ4iD7OCTpV5OW/QzCYDv3P3V0rZ9rS7f6+iF6hbr028/w1KlRyRodWmcrDPPnvXDvUc3zuuX8o55+l3Zx7y69W2cv/Pcfeh5WyrMGGLiNS2UHvVqVK5IyKxEmqvOlVK2iISK6F+PD1VStoiEitqj4iIBCTuq0eUtEUkVtQeEREJiC5EiogERD1tEZGAqD0iIhKQ8j7lHQdK2iISK0WqtEVEwhH39ojuXCMiseLuKY/ymFk7M/urmb1lZqvN7NYofreZvWdmK6LRJ+mYO80s38zWmlnPpHivKJZvZqMP5f2p0haRWKnGSrsQuM3dXzezhsBrZrYg2vZf7v5g8s5mdiowADgNaA0sNLMTo82/BS4DCoDlZjbH3ddUZVJK2iISK9W15M/dtwBboscfm9lbQJtyDrkamOHuXwDvmFk+0DXalu/uGwDMbEa0b5WSttojIhIrRe4pj+T72UYju7RzmtnxwFnA0ih0S3QXrylmtu9+cG2ATUmHFUSxsuJVoqQtIrFSjKc8ku9nG42cA89nZkcDfwRGuvtuYCJwAtCJRCX+0L5dS5mOlxOvErVHRCRWqnP1iJkdQSJhP+XufwJw961J2x8H/hw9LQDaJR3eFtgcPS4rXmmqtEUkVqpx9YgBk4G33P3hpHirpN36AW9Gj+cAA8zsSDNrD2QBy4DlQJaZtTezeiQuVs6p6vtTpS0isVKNlXZ34IfAKjNbEcXGAAPNrBOJFsdG4EYAd19tZs+SuMBYCNzs7kUAZnYLMB/IAKa4++qqTqrcG/tWB93YV0qjG/tKaarjxr5nt74g5ZyzfPPieN3YV0QkNEUe7y9nVdIWkVjRF0aJiAQk7t89oqQtIrGimyCIiASkWO0REZFwqNIWEQmIVo+IiARE7RERkYCoPSIiEhBV2iIiAVGlLSISkKLEdzTFlpK2iMSKPsYuIhIQfYxdRCQgqrRFRAKi1SMiIgHR6hERkYDoY+wiIgFRT1tEJCDqaYuIBESVtohIQLROW0QkIKq0RUQCotUjIiIB0YVIEZGAqD0iIhIQfSJSRCQgqrRFRAIS9562xf1PpcOJmWW7e0665yGHF/1eSGXUSfcEvmKy0z0BOSzp90JSpqQtIhIQJW0RkYAoadcu9S2lNPq9kJTpQqSISEBUaYuIBERJu5aYWS8zW2tm+WY2Ot3zkfQzsylmts3M3kz3XCQcStq1wMwygN8CvYFTgYFmdmp6ZyWHgSeAXumehIRFSbt2dAXy3X2Du+8FZgBXp3lOkmbuvhjYme55SFiUtGtHG2BT0vOCKCYiUilK2rXDSolp2Y6IVJqSdu0oANolPW8LbE7TXEQkYEratWM5kGVm7c2sHjAAmJPmOYlIgJS0a4G7FwK3APOBt4Bn3X11emcl6WZm04ElwElmVmBmQ9M9Jzn86RORIiIBUaUtIhIQJW0RkYAoaYuIBERJW0QkIEraIiIBUdIWEQmIkraISECUtEVEAvL/+e9q3PCd/XEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.822842546980478\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.85      0.90     14996\n",
      "           1       0.27      0.59      0.37      1447\n",
      "\n",
      "    accuracy                           0.82     16443\n",
      "   macro avg       0.61      0.72      0.63     16443\n",
      "weighted avg       0.89      0.82      0.85     16443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree=DecisionTreeClassifier(criterion= 'gini',max_depth= None, max_features= 8, min_samples_leaf= 7)\n",
    "tree.fit(X_train_os,y_train_os)\n",
    "\n",
    "y_pred=tree.predict(xtest)\n",
    "sns.heatmap(confusion_matrix(ytest,y_pred),annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "print(accuracy_score(ytest,y_pred))\n",
    "print(classification_report(ytest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oversapling - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8240588700358815\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.86      0.90     14996\n",
      "           1       0.24      0.46      0.31      1447\n",
      "\n",
      "    accuracy                           0.82     16443\n",
      "   macro avg       0.59      0.66      0.61     16443\n",
      "weighted avg       0.88      0.82      0.85     16443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc=RandomForestClassifier(bootstrap= True,\n",
    " max_depth= 4,\n",
    " max_features ='auto',\n",
    " min_samples_leaf =2,\n",
    " min_samples_split= 2,\n",
    " n_estimators= 17)\n",
    "rfc.fit(X_train_os,y_train_os)\n",
    "\n",
    "y_pred=rfc.predict(xtest)\n",
    "sns.heatmap(confusion_matrix(ytest,y_pred),annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "print(accuracy_score(ytest,y_pred))\n",
    "print(classification_report(ytest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oversampling -XGBC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD4CAYAAAAn3bdmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAa2UlEQVR4nO3deZgV5ZXH8e+hm0VR9kDYVNR2ATUGUFDHGUcioDKCRh1ckRDbieDgligkkUREcdzQuCKguITFFVyxB1FH2RUFEZEWlW5Fkd0Fge575o9btFft5XbT9O23/H146rm3Tr1V963n4Tm8nHqrytwdEREJQ51Md0BERNKnpC0iEhAlbRGRgChpi4gERElbRCQg2bv6B7avXanpKfITp3a+JNNdkFro2VXP2c4eozI5p26LfXf692qaRtoiIgFR0haReEkUp79UwMwmmNkaM3s3JXaTmb1vZovN7Ckza5KybZiZ5ZvZcjPrlRLvHcXyzezqlHgHM5tnZivMbIqZ1auoT0raIhIvxUXpLxV7EOj9o1gecIi7HwZ8AAwDMLOOQH+gU7TP3WaWZWZZwF3AiUBH4KyoLcCNwG3ungNsAAZV1CElbRGJFfdE2kvFx/LXgPU/ir3k7jsy/lygXfS9LzDZ3be6+0dAPnBktOS7+0p33wZMBvqamQHHA49H+08E+lXUJyVtEYmXRCL9Zef9Dngh+t4WKEjZVhjFyoo3Bzam/AOwI14uJW0RiRdPpL2YWa6ZLUxZctP9GTP7M1AEPLojVFpvqhAv1y6f8iciUqPSuMC4g7uPBcZW9ifMbADQB+jh3z91rxBon9KsHfBZ9L20+FqgiZllR6Pt1PZl0khbROKlEiPtqjCz3sBVwCnu/m3KpulAfzOrb2YdgBxgPrAAyIlmitQjebFyepTsZwGnR/sPAKZV9PsaaYtIrHh6s0LSYmaTgOOAFmZWCIwgOVukPpCXvJbIXHf/L3dfamZTgfdIlk0Gu3txdJwhwAwgC5jg7kujn7gKmGxm1wGLgPEV9UlJW0TipXouMALg7meVEi4zsbr7KGBUKfHngedLia8kObskbUraIhIvVSx7hEJJW0TipRIXIkOkpC0i8aKRtohIQKrxQmRtpKQtIvFSjRciayMlbRGJlWiWXWwpaYtIvKimLSISEJVHREQCopG2iEhAirdnuge7lJK2iMSLyiMiIgFReUREJCAaaYuIBERJW0QkHK4LkSIiAVFNW0QkICqPiIgERCNtEZGAaKQtIhIQjbRFRAJSpJcgiIiEQyNtEZGAqKYtIhIQjbRFRAKikbaISEA00hYRCYhmj4iIBMQ90z3YpepkugMiItUqkUh/qYCZTTCzNWb2bkqsmZnlmdmK6LNpFDczu8PM8s1ssZl1TtlnQNR+hZkNSIl3MbMl0T53mJlV1CclbRGJl2pM2sCDQO8fxa4GZrp7DjAzWgc4EciJllzgHkgmeWAE0A04EhixI9FHbXJT9vvxb/2EkraIxIsn0l8qOpT7a8D6H4X7AhOj7xOBfinxhzxpLtDEzFoDvYA8d1/v7huAPKB3tK2Ru89xdwceSjlWmVTTFpF4KS5Ou6mZ5ZIc6e4w1t3HVrBbK3dfDeDuq82sZRRvCxSktCuMYuXFC0uJl0tJW0TipRLztKMEXVGSTldp9WivQrxcKo+ISLxUb027NF9EpQ2izzVRvBBon9KuHfBZBfF2pcTLpaQtIvFSjTXtMkwHdswAGQBMS4mfH80i6Q5sisooM4CeZtY0ugDZE5gRbfvKzLpHs0bOTzlWmVQeEZFY8UT1zdM2s0nAcUALMyskOQtkNDDVzAYBq4AzoubPAycB+cC3wEAAd19vZiOBBVG7a919x8XNP5CcobIb8EK0lEtJW0TipRqfPeLuZ5WxqUcpbR0YXMZxJgATSokvBA6pTJ+UtEUkXioxeyREStoiEi96yp+ISECUtCXVX66/ldfemE+zpk14+pF7Abj5znG8+sY8sutm075ta64bfjmN9tyD2fPfYsy9D7B9exF162ZzxeBBdOtyOADP573C/Q9NAYOWLZoz+po/0rRJY95fsZKRN/2Db7d8R5vWLblxxJ/Yo2HDTJ6yVFKL1i24/LYraPqLpiQ8wYx/vsj0CdMB6HPBf9BnQB+Ki4tZ+PICHrj+AbLrZjP4hiHkHJaDJxKM/dtYlsxd8oNj/nX8Nfxyr1YMPqHUkqmkivkDo5S0K6nfSSdw9m9PYfjIm0tiRx3xay79r4FkZ2dx693jGffwFC6/eBBNmzTizhv/RstfNGfFyo+56LK/8PK0RygqKmb0mHuZ9uh9NG3SmFvuGs8/n3iGwYPOZcToMVw55Pcc8evDePLZGTzw6BNcknt+Bs9YKqu4uJjx143jw3c/ZLeGuzHmudtZ9H+LaNKiKd17dmdIr8EUbSuicfPGAPQ6qxcAQ3oOpnHzxvz9oWu5rM+leJR8jup9NFu+2ZKx8wlOzEfaFc7TNrODzOyq6AlUt0ffD66JztVGXQ8/lMaN9vxB7JhuXcjOzgLgsE4H8cWatQAcfMD+tPxFcwD277A3W7dtY9u2bXj0Z8t33+HufP3Nt7Rs0QyAj1cV0vXwQwE46ojO5L36ek2dmlSTDWs28OG7HwKw5ZstFOQX0PyXzTnpvJN47O7HKNqWfN7zpnWbAGifsxfvvPFOSeybzV+Tc1gOAA12b0C/C/sx5R+TM3AmgUp4+kuAyk3aZnYVMJnk7ZbzSc4zNGCSmV1d3r4/V0899xL/ctQRP4nnvfI6Bx+wH/Xq1aNudjZ/vXIIp573B/697zms/HgVp/VJjrb233cfZr0+F4CXZv0fn3+xtkb7L9WrZbuW7NtpX5YvWk7bDm3pdGQnbpl2KzdMHV2SmD9a9hHde3anTlYdWrVvxX6H7E+LNi0AOPfK83h67FNs3bI1k6cRluLi9JcAVTTSHgQc4e6j3f2RaBlN8vGCg8raycxyzWyhmS0c99Ck6uxvrXbfxElkZWXRp+e//yCev/ITbr17Atf88RIAthcVMeWp53jsgTuZNe1RDtivA+MengrAyOGXMemJZzjzd5fwzbdbqFtXFaxQNdi9AcPv+zP3//1+tny9hazsOuzReA+u6Hs5D4yawFV3J8c9eVNeYu3qtYx59nYuHJHL+28uo7goQYeO+9Jmn9bMmTEnw2cSFk8k0l5CVFFGSABtgE9+FG8dbStV6kNYtq9dGeb/QSpp2vN5vPbGfMbdcQOpzzH/fM2XDB0+kuv/eiV7tWsDwPsrkv913rHeq8exjI+S9r57t+f+MdcDyVLJa7Pn1+RpSDXJys5i+H3DeeWpWcx5cTYAa1evY84Lye8fvPMB7k6jZo3YvH4z4669v2Tfm568mc8+/pRDuh3Kfofuz/g3JpCVnUXj5o25YcoNDPvPYRk5p2AEWvZIV0VJ+1Jgppmt4PtHC+4F7A8M2ZUdC8nrcxcy/tHHePDO/2G3Bg1K4pu/+pqL/ziCSy+6gM6HdSqJt2rRgg8/XsX6DRtp1rQJc+YvYt999gJg3YaNNG/ahEQiwX0TJ3Nmv5Nq/Hxk5w29aSgF+QU8Pe7pktjcl+Zw2NG/YsncJbTp0IbsutlsXr+Z+g3qg8HWLVs5/NjDKS4upmBFAQUrCnjhkeeBZJllxAMjlLDT8XN+sa+7v2hmB5Ash7QlWc8uBBa4e5gFoZ30xxGjWbBoMRs3bqZHv3O5eNB5jHt4Ctu2b+fCS/8MJC9GjvjTJUx64hkKCj/j3gcnce+DyTLR2DGjaPmL5vxh4DkMGPwnsrOzaPPLloz68xVAcirg5CefBeA3/3Y0p57cMzMnKlXW8YiOHP/bHny07CPueOEfADz0PxPJm5LH0Jsu5a68u9i+rYjbLr8VgMYtGnPtwyPxhLPui3XccunN5R1eKhLzkbb5Lp7T+HMpj0jlnNr5kkx3QWqhZ1c9V+E7EivyzTX90845Da+dvNO/V9N0lUtE4uXnXB4REQlOzMsjStoiEiuhTuVLl5K2iMSLRtoiIgFR0hYRCUigt6enS0lbRGKlOt8RWRspaYtIvChpi4gERLNHREQCopG2iEhAlLRFRMLhxSqPiIiEQyNtEZFwaMqfiEhIYp60K3wbu4hIUBKVWCpgZpeZ2VIze9fMJplZAzPrYGbzzGyFmU0xs3pR2/rRen60fZ+U4wyL4svNrNfOnJ6StojEihcl0l7KY2Ztgf8Gurr7IUAW0B+4EbjN3XOADXz/kvNBwAZ33x+4LWqHmXWM9usE9AbuNrOsqp6fkraIxEs1jrRJlpB3M7NsYHdgNXA88Hi0fSLQL/reN1on2t7Dkm/57gtMdvet7v4RkE/yFY5VoqQtIrHiCU97MbNcM1uYsuSWHMf9U+BmYBXJZL0JeBPY6O5FUbNCku/PJfosiPYtito3T42Xsk+l6UKkiMRLJaZpu/tYYGxp28ysKclRcgdgI/AYcGJph9mxSxnbyopXiZK2iMRKNU75+w3wkbt/CWBmTwJHA03MLDsaTbcDPovaFwLtgcKonNIYWJ8S3yF1n0pTeURE4qX6atqrgO5mtntUm+4BvAfMAk6P2gwApkXfp0frRNtfdneP4v2j2SUdgBxgflVPTyNtEYmVkmrzzh7HfZ6ZPQ68BRQBi0iWUp4DJpvZdVFsfLTLeOBhM8snOcLuHx1nqZlNJZnwi4DB7l7lNzUoaYtIrHg1PnrE3UcAI34UXkkpsz/c/TvgjDKOMwoYVR19UtIWkXiJ9/OilLRFJF6qc6RdGylpi0isKGmLiATEi0ubFh0fStoiEisaaYuIBMQTGmmLiARDI20RkYC4a6QtIhIMjbRFRAKS0OwREZFw6EKkiEhAlLRFRALi8X4Zu5K2iMSLRtoiIgHRlD8RkYAUa/aIiEg4NNIWEQmIatoiIgHR7BERkYBopC0iEpDiRJ1Md2GXUtIWkVhReUREJCAJzR4REQmHpvyJiARE5ZGdtFubY3f1T0iAWjVskukuSEypPCIiEhDNHhERCUjMqyPE+58kEfnZSbilvVTEzJqY2eNm9r6ZLTOzo8ysmZnlmdmK6LNp1NbM7A4zyzezxWbWOeU4A6L2K8xswM6cn5K2iMSKu6W9pOF24EV3Pwj4FbAMuBqY6e45wMxoHeBEICdacoF7AMysGTAC6AYcCYzYkeirQklbRGIlUYmlPGbWCPhXYDyAu29z941AX2Bi1Gwi0C/63hd4yJPmAk3MrDXQC8hz9/XuvgHIA3pX9fyUtEUkVhxLezGzXDNbmLLkphxqX+BL4AEzW2Rm48ysIdDK3VcDRJ8to/ZtgYKU/QujWFnxKtGFSBGJlaJKTPlz97HA2DI2ZwOdgUvcfZ6Z3c73pZDSlPbDXk68SjTSFpFYqcxIuwKFQKG7z4vWHyeZxL+Iyh5En2tS2rdP2b8d8Fk58SpR0haRWKmumra7fw4UmNmBUagH8B4wHdgxA2QAMC36Ph04P5pF0h3YFJVPZgA9zaxpdAGyZxSrEpVHRCRW0hhBV8YlwKNmVg9YCQwkOdidamaDgFXAGVHb54GTgHzg26gt7r7ezEYCC6J217r7+qp2SElbRGKlohF0Zbj720DXUjb1KKWtA4PLOM4EYEJ19ElJW0Ripbh6R9q1jpK2iMRKzN82pqQtIvGS0EhbRCQccX9glJK2iMRKdV6IrI2UtEUkVhKm8oiISDCKM92BXUxJW0RiRbNHREQCotkjIiIB0ewREZGAqDwiIhIQTfkTEQlIsUbaIiLh0EhbRCQgStoiIgGpxCsig6SkLSKxopG2iEhAdBu7iEhANE9bRCQgKo+IiARESVtEJCB69oiISEBU0xYRCYhmj4iIBCQR8wKJkraIxIouRIqIBCTe42yok+kOiIhUp0QllnSYWZaZLTKzZ6P1DmY2z8xWmNkUM6sXxetH6/nR9n1SjjEsii83s147c35K2iISK0XmaS9pGgosS1m/EbjN3XOADcCgKD4I2ODu+wO3Re0ws45Af6AT0Bu428yyqnp+StoiEiteiaUiZtYOOBkYF60bcDzweNRkItAv+t43Wifa3iNq3xeY7O5b3f0jIB84sqrnp6QtIrFSmfKImeWa2cKUJfdHhxsD/InvqynNgY3uXhStFwJto+9tgQKAaPumqH1JvJR9Kk0XIkUkVioz5c/dxwJjS9tmZn2ANe7+ppkdtyNc2mEq2FbePpWmpC0isVKNs0eOAU4xs5OABkAjkiPvJmaWHY2m2wGfRe0LgfZAoZllA42B9SnxHVL3qTSVR0QkVqpr9oi7D3P3du6+D8kLiS+7+znALOD0qNkAYFr0fXq0TrT9ZXf3KN4/ml3SAcgB5lf1/DTSFpFYKd71M7WvAiab2XXAImB8FB8PPGxm+SRH2P0B3H2pmU0F3gOKgMHuXuW77ZW0RSRWdsUdke7+CvBK9H0lpcz+cPfvgDPK2H8UMKo6+qKkLSKx4jG/J1JJW0RiJe7PHtGFyGpSv3595rzxLG8uzOOdt19mxDVXADD2vpt5c2Eeb72Zx5TJY2nYcHcALh2ay+J3ZvHWm3m89OIU9tqrytM2pRa65R8jeeeD15g5++mSWJ++PXl59jQK1i3hsMM7lcSPPe4oXpg1lf994ylemDWVY47tVrKt729P4n/feIq815/kkcfuo2mzJjV6HiFK4GkvIVLSriZbt27lNz3PpEvXE+jStSe9eh5HtyM7c8WVf6NL1xPo3OUEClZ9yuCLBwLw9tvv0q37iXTucgJPPPkco2/4S4bPQKrT1ElPc87pF/0g9v6yfC48fyhzZy/8QXz9ug1ccNZgfnPMqVx68XBuv/cGALKysrj2hqs54z8GcsK/nMay9z5g4IVn19g5hKo674isjZS0q9E333wLQN262WTXrYu789VXX5dsb7BbA5IzgOCVV2ezZct3AMyb/ybt2rau+Q7LLjNv9pts3LDpB7H8D1byYf7HP2m7dMn7fPH5lwAsX5ZPgwb1qVevLmaGmbF7w90A2HPPhiXtpGxFeNpLiJS0q1GdOnVYuOAlVn+6mJkzX2P+gkUAjLv/Vj4teJuDDtyfO++a8JP9Bl5wFi/OmFXT3ZVa6ORTevLu4mVs27adoqIihl0xkpmvP81by14h58D9mPTwE5nuYq3nlfgToionbTMbWM62kvv5E4lvqvoTwUkkEnQ9oid7d+jKEV1/TadOBwLw+wsvp/3enVn2/grOPOOUH+xz9tmn0bXLr7j5lnsy0WWpRQ44aD+G/+0yrrrs7wBkZ2dz/u/+k17/djqdDz6OZUs/4JLLLsxwL2u/6n40a22zMyPtv5e1wd3HuntXd+9ap07DnfiJMG3atJlXX5tNr57HlcQSiQSPPTad0049uSTW4/hjGXb1f9PvtAvYtm1bBnoqtUXrNq0Y//AdDP3DcD75OPlsoU6HHgRQsv7M0y/SpdvhGetjKH7WI20zW1zGsgRoVUN9DEKLFs1o3LgRAA0aNKDH8cfywQcr2W+/fUra9Dn5BJYvzwfg8MM7cfddozn1tIF8+eW6THRZaolGjfbkoSn3cMO1Y1g4b1FJ/PPVX5Bz4H40a94UgH897mjyl6/MVDeDEfeRtu24MFbqRrMvgF4kH/T9g03AbHdvU9EPZNdrG+Y/Z5V06KEHM2H8GLKy6lCnTh0ef/wZRl1/O6/Oeoo9G+2BmbF48XsMHjKMr776mhkvTOaQQw5i9edrACgo+JRTTyuz4hQ7rRrGe+raXeNu4qhjjqBZ8yasXbOOm0ffxcYNm7juxuE0a9GMzZs2s3TJcs45PZehV1zEkMt+z0crV5Xsf9ZpF7Ju7XrOG3gmgy46l+1FRXxasJrLLh7Ohh9d4IyTTzcsLe2JeJVy7t6npZ1zHvnkyZ3+vZpWUdIeDzzg7q+Xsu2f7l7h/KOfS9KWyol70paqqY6kffbep6adc/75yVPBJe1y74h090HlbNOEURGpdUKtVadLt7GLSKyEWqtOl5K2iMRKqLenp0tJW0RiReUREZGAFJczuSIOlLRFJFZUHhERCYguRIqIBEQ1bRGRgKg8IiISkPLu8o4DJW0RiZVijbRFRMKh8oiISEBUHhERCYhG2iIiAdGUPxGRgMT9Nna9jV1EYiWBp72Ux8zam9ksM1tmZkvNbGgUb2ZmeWa2IvpsGsXNzO4ws/zotYydU441IGq/wswG7Mz5KWmLSKxUV9IGioAr3P1goDsw2Mw6AlcDM909B5gZrQOcCORESy5wDySTPDAC6AYcCYzYkeirQklbRGLF3dNeKjjOand/K/r+FbAMaAv0BSZGzSYC/aLvfYGHPGku0MTMWpN8z26eu6939w1AHtC7quenpC0isVKZkbaZ5ZrZwpQlt7Rjmtk+wK+BeUArd18NycQOtIyatQUKUnYrjGJlxatEFyJFJFYqM3vE3ccCY8trY2Z7AE8Al7r7ZrMy3wVc2gYvJ14lGmmLSKwUeyLtpSJmVpdkwn7U3Z+Mwl9EZQ+izzVRvBBon7J7O+CzcuJVoqQtIrFSXTVtSw6pxwPL3P3WlE3TgR0zQAYA01Li50ezSLoDm6LyyQygp5k1jS5A9oxiVaLyiIjESjXeEXkMcB6wxMzejmLDgdHAVDMbBKwCzoi2PQ+cBOQD3wIDAdx9vZmNBBZE7a519/VV7ZTt6vv0s+u1jfdMd6mSVg2bZLoLUgt9umFpmQXjdB32y6PSzjmLP5+z079X0zTSFpFYScT8jkglbRGJFT17REQkIOnMCgmZkraIxIrKIyIiAVF5REQkIBppi4gERCNtEZGAFHtxpruwSylpi0is6MW+IiIB0Yt9RUQCopG2iEhANHtERCQgmj0iIhIQ3cYuIhIQ1bRFRAKimraISEA00hYRCYjmaYuIBEQjbRGRgGj2iIhIQHQhUkQkICqPiIgERHdEiogERCNtEZGAxL2mbXH/V6k2MbNcdx+b6X5I7aK/F1IZdTLdgZ+Z3Ex3QGol/b2QtClpi4gERElbRCQgSto1S3VLKY3+XkjadCFSRCQgGmmLiARESVtEJCBK2jXEzHqb2XIzyzezqzPdH8k8M5tgZmvM7N1M90XCoaRdA8wsC7gLOBHoCJxlZh0z2yupBR4Eeme6ExIWJe2acSSQ7+4r3X0bMBnom+E+SYa5+2vA+kz3Q8KipF0z2gIFKeuFUUxEpFKUtGuGlRLTXEsRqTQl7ZpRCLRPWW8HfJahvohIwJS0a8YCIMfMOphZPaA/MD3DfRKRAClp1wB3LwKGADOAZcBUd1+a2V5JppnZJGAOcKCZFZrZoEz3SWo/3cYuIhIQjbRFRAKipC0iEhAlbRGRgChpi4gERElbRCQgStoiIgFR0hYRCcj/A4Sx+MhzLLCFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8159703217174481\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.82      0.89     14983\n",
      "           1       0.30      0.77      0.43      1460\n",
      "\n",
      "    accuracy                           0.82     16443\n",
      "   macro avg       0.63      0.80      0.66     16443\n",
      "weighted avg       0.91      0.82      0.85     16443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "xgb=XGBClassifier(colsample_bytree= 0.8, gamma =1.5, max_depth =4, min_child_weight =1, subsample= 1.0)\n",
    "xgb.fit(X_train_os,y_train_os)\n",
    "\n",
    "y_pred=xgb.predict(xtest)\n",
    "sns.heatmap(confusion_matrix(ytest,y_pred),annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "print(accuracy_score(ytest,y_pred))\n",
    "print(classification_report(ytest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of classes before fit Counter({0: 35144, 1: 3221})\n",
      "The number of classes after fit Counter({0: 34838, 1: 26052})\n"
     ]
    }
   ],
   "source": [
    "from imblearn.combine import SMOTETomek\n",
    "os=SMOTETomek(0.75)\n",
    "X_train_ns,y_train_ns=os.fit_resample(xtrain,ytrain)\n",
    "print(\"The number of classes before fit {}\".format(Counter(ytrain)))\n",
    "print(\"The number of classes after fit {}\".format(Counter(y_train_ns)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOT - Logistic "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7888463175819498\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.79      0.87     14996\n",
      "           1       0.26      0.74      0.38      1447\n",
      "\n",
      "    accuracy                           0.79     16443\n",
      "   macro avg       0.61      0.77      0.63     16443\n",
      "weighted avg       0.91      0.79      0.83     16443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logistic2=LogisticRegression(C= 100, penalty='l2')\n",
    "logistic2.fit(X_train_ns,y_train_ns)\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "y_pred=logistic2.predict(xtest)\n",
    "sns.heatmap(confusion_matrix(ytest,y_pred),annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "print(accuracy_score(ytest,y_pred))\n",
    "print(classification_report(ytest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOT - Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8513045064769202\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.89      0.92     14996\n",
      "           1       0.29      0.47      0.36      1447\n",
      "\n",
      "    accuracy                           0.85     16443\n",
      "   macro avg       0.62      0.68      0.64     16443\n",
      "weighted avg       0.89      0.85      0.87     16443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree2=DecisionTreeClassifier(criterion= 'gini',max_depth= None, max_features= 8, min_samples_leaf= 7)\n",
    "tree2.fit(X_train_ns,y_train_ns)\n",
    "\n",
    "y_pred=tree2.predict(xtest)\n",
    "sns.heatmap(confusion_matrix(ytest,y_pred),annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "print(accuracy_score(ytest,y_pred))\n",
    "print(classification_report(ytest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOT - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8213221431612237\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.86      0.90     14996\n",
      "           1       0.23      0.45      0.31      1447\n",
      "\n",
      "    accuracy                           0.82     16443\n",
      "   macro avg       0.59      0.65      0.60     16443\n",
      "weighted avg       0.88      0.82      0.85     16443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc2=RandomForestClassifier(bootstrap= True,\n",
    " max_depth= 4,\n",
    " max_features ='auto',\n",
    " min_samples_leaf =2,\n",
    " min_samples_split= 2,\n",
    " n_estimators= 17)\n",
    "rfc2.fit(X_train_ns,y_train_ns)\n",
    "\n",
    "y_pred=rfc2.predict(xtest)\n",
    "sns.heatmap(confusion_matrix(ytest,y_pred),annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "print(accuracy_score(ytest,y_pred))\n",
    "print(classification_report(ytest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOT-XGBC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9367512011190172\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.97     14996\n",
      "           1       0.86      0.34      0.48      1447\n",
      "\n",
      "    accuracy                           0.94     16443\n",
      "   macro avg       0.90      0.67      0.72     16443\n",
      "weighted avg       0.93      0.94      0.92     16443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgb2=XGBClassifier(colsample_bytree= 0.8, gamma =1.5, max_depth =4, min_child_weight =1, subsample= 1.0)\n",
    "xgb2.fit(X_train_ns,y_train_ns)\n",
    "\n",
    "y_pred=xgb2.predict(xtest)\n",
    "sns.heatmap(confusion_matrix(ytest,y_pred),annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "print(accuracy_score(ytest,y_pred))\n",
    "print(classification_report(ytest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By analyizing all of the above aspects we can conclude that we should take oversampling- XGBC as it is able to classify maximum no of 1's"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dumping the model for deployment purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(xgb, open(\"pima2.pickle.dat\", \"wb\"))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}