Bagging Algorithms.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"S:/ML/Datasets/diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging\n",
    "\n",
    "The idea behind bagging is combining the results of multiple models (for instance, all decision trees) to get a generalized result. \n",
    "Bootstrapping is a sampling technique in which we create subsets of observations from the original dataset, with replacement. The size of the subsets is the same as the size of the original set.\n",
    "\n",
    "Bagging (or Bootstrap Aggregating) technique uses these subsets (bags) to get a fair idea of the distribution (complete set). The size of subsets created for bagging may be less than the original set.\n",
    "\n",
    "1.Multiple subsets are created from the original dataset, selecting observations with replacement.\n",
    "2.A base model (weak model) is created on each of these subsets.\n",
    "3.The models run in parallel and are independent of each other.\n",
    "4.The final predictions are determined by combining the predictions from all the models.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging Meta Estimator Classifier\n",
    "\n",
    "Bagging meta-estimator\n",
    "\n",
    "Bagging meta-estimator is an ensembling algorithm that can be used for both classification (BaggingClassifier) and regression (BaggingRegressor) problems. It follows the typical bagging technique to make predictions. Following are the steps for the bagging meta-estimator algorithm:\n",
    "\n",
    "    1.Random subsets are created from the original dataset (Bootstrapping).\n",
    "    2.The subset of the dataset includes all features.\n",
    "    3.A user-specified base estimator is fitted on each of these smaller sets.\n",
    "    4.Predictions from each model are combined to get the final result.\n",
    "\n",
    "#### Parameters used in the  algorithms:\n",
    "\n",
    "    base_estimator:\n",
    "        It defines the base estimator to fit on random subsets of the dataset.\n",
    "        When nothing is specified, the base estimator is a decision tree.\n",
    "    n_estimators:\n",
    "        It is the number of base estimators to be created.\n",
    "        The number of estimators should be carefully tuned as a large number would take a very long time to run, while a very small number might not provide the best results.\n",
    "    max_samples:\n",
    "        This parameter controls the size of the subsets.\n",
    "        It is the maximum number of samples to train each base estimator.\n",
    "    max_features:\n",
    "        Controls the number of features to draw from the whole dataset.\n",
    "        It defines the maximum number of features required to train each base estimator.\n",
    "    n_jobs:\n",
    "        The number of jobs to run in parallel.\n",
    "        Set this value equal to the cores in your system.\n",
    "        If -1, the number of jobs is set to the number of cores.\n",
    "\n",
    "    random_state:\n",
    "        It specifies the method of random split. When random state value is same for two models, the random selection is same for both models.\n",
    "        This parameter is useful when you want to compare different models.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(columns='Outcome')\n",
    "y=df[\"Outcome\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy is : 0.8181818181818182\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.91      0.87        99\n",
      "           1       0.80      0.65      0.72        55\n",
      "\n",
      "    accuracy                           0.82       154\n",
      "   macro avg       0.81      0.78      0.79       154\n",
      "weighted avg       0.82      0.82      0.81       154\n",
      "\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"
    }
   ],
   "source": [
    "#Bagging Meta Estimator Classifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "#making the bagging classifier with 100 decision trees\n",
    "model=BaggingClassifier(n_estimators=100)\n",
    "#fitting data to bagging model\n",
    "model.fit(xtrain,ytrain)\n",
    "#testing on test dataset \n",
    "ypred=model.predict(xtest)\n",
    "#Model Evaluation \n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "print(\"accuracy is :\",accuracy_score(ytest,ypred))\n",
    "cm=confusion_matrix(ytest,ypred)\n",
    "sns.heatmap(cm,annot=True)\n",
    "print(classification_report(ytest,ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class BaggingClassifier in module sklearn.ensemble._bagging:\n",
      "\n",
      "class BaggingClassifier(sklearn.base.ClassifierMixin, BaseBagging)\n",
      " |  BaggingClassifier(base_estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0)\n",
      " |  \n",
      " |  A Bagging classifier.\n",
      " |  \n",
      " |  A Bagging classifier is an ensemble meta-estimator that fits base\n",
      " |  classifiers each on random subsets of the original dataset and then\n",
      " |  aggregate their individual predictions (either by voting or by averaging)\n",
      " |  to form a final prediction. Such a meta-estimator can typically be used as\n",
      " |  a way to reduce the variance of a black-box estimator (e.g., a decision\n",
      " |  tree), by introducing randomization into its construction procedure and\n",
      " |  then making an ensemble out of it.\n",
      " |  \n",
      " |  This algorithm encompasses several works from the literature. When random\n",
      " |  subsets of the dataset are drawn as random subsets of the samples, then\n",
      " |  this algorithm is known as Pasting [1]_. If samples are drawn with\n",
      " |  replacement, then the method is known as Bagging [2]_. When random subsets\n",
      " |  of the dataset are drawn as random subsets of the features, then the method\n",
      " |  is known as Random Subspaces [3]_. Finally, when base estimators are built\n",
      " |  on subsets of both samples and features, then the method is known as\n",
      " |  Random Patches [4]_.\n",
      " |  \n",
      " |  Read more in the :ref:`User Guide <bagging>`.\n",
      " |  \n",
      " |  .. versionadded:: 0.15\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  base_estimator : object, default=None\n",
      " |      The base estimator to fit on random subsets of the dataset.\n",
      " |      If None, then the base estimator is a\n",
      " |      :class:`~sklearn.tree.DecisionTreeClassifier`.\n",
      " |  \n",
      " |  n_estimators : int, default=10\n",
      " |      The number of base estimators in the ensemble.\n",
      " |  \n",
      " |  max_samples : int or float, default=1.0\n",
      " |      The number of samples to draw from X to train each base estimator (with\n",
      " |      replacement by default, see `bootstrap` for more details).\n",
      " |  \n",
      " |      - If int, then draw `max_samples` samples.\n",
      " |      - If float, then draw `max_samples * X.shape[0]` samples.\n",
      " |  \n",
      " |  max_features : int or float, default=1.0\n",
      " |      The number of features to draw from X to train each base estimator (\n",
      " |      without replacement by default, see `bootstrap_features` for more\n",
      " |      details).\n",
      " |  \n",
      " |      - If int, then draw `max_features` features.\n",
      " |      - If float, then draw `max_features * X.shape[1]` features.\n",
      " |  \n",
      " |  bootstrap : bool, default=True\n",
      " |      Whether samples are drawn with replacement. If False, sampling\n",
      " |      without replacement is performed.\n",
      " |  \n",
      " |  bootstrap_features : bool, default=False\n",
      " |      Whether features are drawn with replacement.\n",
      " |  \n",
      " |  oob_score : bool, default=False\n",
      " |      Whether to use out-of-bag samples to estimate\n",
      " |      the generalization error. Only available if bootstrap=True.\n",
      " |  \n",
      " |  warm_start : bool, default=False\n",
      " |      When set to True, reuse the solution of the previous call to fit\n",
      " |      and add more estimators to the ensemble, otherwise, just fit\n",
      " |      a whole new ensemble. See :term:`the Glossary <warm_start>`.\n",
      " |  \n",
      " |      .. versionadded:: 0.17\n",
      " |         *warm_start* constructor parameter.\n",
      " |  \n",
      " |  n_jobs : int, default=None\n",
      " |      The number of jobs to run in parallel for both :meth:`fit` and\n",
      " |      :meth:`predict`. ``None`` means 1 unless in a\n",
      " |      :obj:`joblib.parallel_backend` context. ``-1`` means using all\n",
      " |      processors. See :term:`Glossary <n_jobs>` for more details.\n",
      " |  \n",
      " |  random_state : int, RandomState instance or None, default=None\n",
      " |      Controls the random resampling of the original dataset\n",
      " |      (sample wise and feature wise).\n",
      " |      If the base estimator accepts a `random_state` attribute, a different\n",
      " |      seed is generated for each instance in the ensemble.\n",
      " |      Pass an int for reproducible output across multiple function calls.\n",
      " |      See :term:`Glossary <random_state>`.\n",
      " |  \n",
      " |  verbose : int, default=0\n",
      " |      Controls the verbosity when fitting and predicting.\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  base_estimator_ : estimator\n",
      " |      The base estimator from which the ensemble is grown.\n",
      " |  \n",
      " |  n_features_ : int\n",
      " |      The number of features when :meth:`fit` is performed.\n",
      " |  \n",
      " |  estimators_ : list of estimators\n",
      " |      The collection of fitted base estimators.\n",
      " |  \n",
      " |  estimators_samples_ : list of arrays\n",
      " |      The subset of drawn samples (i.e., the in-bag samples) for each base\n",
      " |      estimator. Each subset is defined by an array of the indices selected.\n",
      " |  \n",
      " |  estimators_features_ : list of arrays\n",
      " |      The subset of drawn features for each base estimator.\n",
      " |  \n",
      " |  classes_ : ndarray of shape (n_classes,)\n",
      " |      The classes labels.\n",
      " |  \n",
      " |  n_classes_ : int or list\n",
      " |      The number of classes.\n",
      " |  \n",
      " |  oob_score_ : float\n",
      " |      Score of the training dataset obtained using an out-of-bag estimate.\n",
      " |      This attribute exists only when ``oob_score`` is True.\n",
      " |  \n",
      " |  oob_decision_function_ : ndarray of shape (n_samples, n_classes)\n",
      " |      Decision function computed with out-of-bag estimate on the training\n",
      " |      set. If n_estimators is small it might be possible that a data point\n",
      " |      was never left out during the bootstrap. In this case,\n",
      " |      `oob_decision_function_` might contain NaN. This attribute exists\n",
      " |      only when ``oob_score`` is True.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> from sklearn.svm import SVC\n",
      " |  >>> from sklearn.ensemble import BaggingClassifier\n",
      " |  >>> from sklearn.datasets import make_classification\n",
      " |  >>> X, y = make_classification(n_samples=100, n_features=4,\n",
      " |  ...                            n_informative=2, n_redundant=0,\n",
      " |  ...                            random_state=0, shuffle=False)\n",
      " |  >>> clf = BaggingClassifier(base_estimator=SVC(),\n",
      " |  ...                         n_estimators=10, random_state=0).fit(X, y)\n",
      " |  >>> clf.predict([[0, 0, 0, 0]])\n",
      " |  array([1])\n",
      " |  \n",
      " |  References\n",
      " |  ----------\n",
      " |  \n",
      " |  .. [1] L. Breiman, \"Pasting small votes for classification in large\n",
      " |         databases and on-line\", Machine Learning, 36(1), 85-103, 1999.\n",
      " |  \n",
      " |  .. [2] L. Breiman, \"Bagging predictors\", Machine Learning, 24(2), 123-140,\n",
      " |         1996.\n",
      " |  \n",
      " |  .. [3] T. Ho, \"The random subspace method for constructing decision\n",
      " |         forests\", Pattern Analysis and Machine Intelligence, 20(8), 832-844,\n",
      " |         1998.\n",
      " |  \n",
      " |  .. [4] G. Louppe and P. Geurts, \"Ensembles on Random Patches\", Machine\n",
      " |         Learning and Knowledge Discovery in Databases, 346-361, 2012.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      BaggingClassifier\n",
      " |      sklearn.base.ClassifierMixin\n",
      " |      BaseBagging\n",
      " |      sklearn.ensemble._base.BaseEnsemble\n",
      " |      sklearn.base.MetaEstimatorMixin\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, base_estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  decision_function(self, X)\n",
      " |      Average of the decision functions of the base classifiers.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
      " |          The training input samples. Sparse matrices are accepted only if\n",
      " |          they are supported by the base estimator.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : ndarray of shape (n_samples, k)\n",
      " |          The decision function of the input samples. The columns correspond\n",
      " |          to the classes in sorted order, as they appear in the attribute\n",
      " |          ``classes_``. Regression and binary classification are special\n",
      " |          cases with ``k == 1``, otherwise ``k==n_classes``.\n",
      " |  \n",
      " |  predict(self, X)\n",
      " |      Predict class for X.\n",
      " |      \n",
      " |      The predicted class of an input sample is computed as the class with\n",
      " |      the highest mean predicted probability. If base estimators do not\n",
      " |      implement a ``predict_proba`` method, then it resorts to voting.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
      " |          The training input samples. Sparse matrices are accepted only if\n",
      " |          they are supported by the base estimator.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y : ndarray of shape (n_samples,)\n",
      " |          The predicted classes.\n",
      " |  \n",
      " |  predict_log_proba(self, X)\n",
      " |      Predict class log-probabilities for X.\n",
      " |      \n",
      " |      The predicted class log-probabilities of an input sample is computed as\n",
      " |      the log of the mean predicted class probabilities of the base\n",
      " |      estimators in the ensemble.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
      " |          The training input samples. Sparse matrices are accepted only if\n",
      " |          they are supported by the base estimator.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      p : ndarray of shape (n_samples, n_classes)\n",
      " |          The class log-probabilities of the input samples. The order of the\n",
      " |          classes corresponds to that in the attribute :term:`classes_`.\n",
      " |  \n",
      " |  predict_proba(self, X)\n",
      " |      Predict class probabilities for X.\n",
      " |      \n",
      " |      The predicted class probabilities of an input sample is computed as\n",
      " |      the mean predicted class probabilities of the base estimators in the\n",
      " |      ensemble. If base estimators do not implement a ``predict_proba``\n",
      " |      method, then it resorts to voting and the predicted class probabilities\n",
      " |      of an input sample represents the proportion of estimators predicting\n",
      " |      each class.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
      " |          The training input samples. Sparse matrices are accepted only if\n",
      " |          they are supported by the base estimator.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      p : ndarray of shape (n_samples, n_classes)\n",
      " |          The class probabilities of the input samples. The order of the\n",
      " |          classes corresponds to that in the attribute :term:`classes_`.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.ClassifierMixin:\n",
      " |  \n",
      " |  score(self, X, y, sample_weight=None)\n",
      " |      Return the mean accuracy on the given test data and labels.\n",
      " |      \n",
      " |      In multi-label classification, this is the subset accuracy\n",
      " |      which is a harsh metric since you require for each sample that\n",
      " |      each label set be correctly predicted.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Test samples.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
      " |          True labels for `X`.\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Sample weights.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : float\n",
      " |          Mean accuracy of ``self.predict(X)`` wrt. `y`.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.ClassifierMixin:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from BaseBagging:\n",
      " |  \n",
      " |  fit(self, X, y, sample_weight=None)\n",
      " |      Build a Bagging ensemble of estimators from the training\n",
      " |         set (X, y).\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
      " |          The training input samples. Sparse matrices are accepted only if\n",
      " |          they are supported by the base estimator.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,)\n",
      " |          The target values (class labels in classification, real numbers in\n",
      " |          regression).\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Sample weights. If None, then samples are equally weighted.\n",
      " |          Note that this is supported only if the base estimator supports\n",
      " |          sample weighting.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties inherited from BaseBagging:\n",
      " |  \n",
      " |  estimators_samples_\n",
      " |      The subset of drawn samples for each base estimator.\n",
      " |      \n",
      " |      Returns a dynamically generated list of indices identifying\n",
      " |      the samples used for fitting each member of the ensemble, i.e.,\n",
      " |      the in-bag samples.\n",
      " |      \n",
      " |      Note: the list is re-created at each call to the property in order\n",
      " |      to reduce the object memory footprint by not storing the sampling\n",
      " |      data. Thus fetching the property may be slower than expected.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.ensemble._base.BaseEnsemble:\n",
      " |  \n",
      " |  __getitem__(self, index)\n",
      " |      Return the index'th estimator in the ensemble.\n",
      " |  \n",
      " |  __iter__(self)\n",
      " |      Return iterator over estimators in the ensemble.\n",
      " |  \n",
      " |  __len__(self)\n",
      " |      Return the number of estimators in the ensemble.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from sklearn.ensemble._base.BaseEnsemble:\n",
      " |  \n",
      " |  __annotations__ = {'_required_parameters': typing.List[str]}\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self, N_CHAR_MAX=700)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : bool, default=True\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : dict\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n",
      " |      parameters of the form ``<component>__<parameter>`` so that it's\n",
      " |      possible to update each component of a nested object.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      **params : dict\n",
      " |          Estimator parameters.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : estimator instance\n",
      " |          Estimator instance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(BaggingClassifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing The Bagging Classifier with Logistic Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bagging Meta Estimator Classifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "#making the bagging classifier with 100 Logistic Regression models\n",
    "model=BaggingClassifier(base_estimator=LogisticRegression(),n_estimators=100)\n",
    "#fitting data to bagging model\n",
    "model.fit(xtrain,ytrain)\n",
    "#testing on test dataset \n",
    "ypred=model.predict(xtest)\n",
    "#Model Evaluation \n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "print(\"accuracy is :\",accuracy_score(ytest,ypred))\n",
    "cm=confusion_matrix(ytest,ypred)\n",
    "sns.heatmap(cm,annot=True)\n",
    "print(classification_report(ytest,ypred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classifier\n",
    "\n",
    "Random Forest is another ensemble machine learning algorithm that follows the bagging technique. It is an extension of the bagging estimator algorithm. The base estimators in random forest are decision trees. Unlike bagging meta estimator, random forest randomly selects a set of features which are used to decide the best split at each node of the decision tree.\n",
    "\n",
    "Looking at it step-by-step, this is what a random forest model does:\n",
    "\n",
    "    1.Random subsets are created from the original dataset (bootstrapping).\n",
    "    2.At each node in the decision tree, only a random set of features are considered to decide the best split.\n",
    "    3.A decision tree model is fitted on each of the subsets.\n",
    "    4.The final prediction is calculated by averaging the predictions from all decision trees.\n",
    "\n",
    "Note: The decision trees in random forest can be built on a subset of data and features. Particularly, the sklearn model of random forest uses all features for decision tree and a subset of features are randomly selected for splitting at each node.\n",
    "\n",
    "To sum up, Random forest randomly selects data points and features, and builds multiple trees (Forest) .\n",
    "\n",
    "Parameters\n",
    "\n",
    "    n_estimators:\n",
    "        It defines the number of decision trees to be created in a random forest.\n",
    "        Generally, a higher number makes the predictions stronger and more stable, but a very large number can result in higher training time.\n",
    "    criterion:\n",
    "        It defines the function that is to be used for splitting.\n",
    "        The function measures the quality of a split for each feature and chooses the best split.\n",
    "    max_features :\n",
    "        It defines the maximum number of features allowed for the split in each decision tree.\n",
    "        Increasing max features usually improve performance but a very high number can decrease the diversity of each tree.\n",
    "    max_depth:\n",
    "        Random forest has multiple decision trees. This parameter defines the maximum depth of the trees.\n",
    "    min_samples_split:\n",
    "        Used to define the minimum number of samples required in a leaf node before a split is attempted.\n",
    "        If the number of samples is less than the required number, the node is not split.\n",
    "    min_samples_leaf:\n",
    "        This defines the minimum number of samples required to be at a leaf node.\n",
    "        Smaller leaf size makes the model more prone to capturing noise in train data.\n",
    "    max_leaf_nodes:\n",
    "        This parameter specifies the maximum number of leaf nodes for each tree.\n",
    "        The tree stops splitting when the number of leaf nodes becomes equal to the max leaf node.\n",
    "    n_jobs:\n",
    "        This indicates the number of jobs to run in parallel.\n",
    "        Set value to -1 if you want it to run on all cores in the system.\n",
    "    random_state:\n",
    "        This parameter is used to define the random selection.\n",
    "        It is used for comparison between various models.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier(n_estimators=100)\n",
    "#fitting data to Random Forest model\n",
    "model.fit(xtrain,ytrain)\n",
    "#testing on test dataset \n",
    "ypred=model.predict(xtest)\n",
    "#Model Evaluation \n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "print(\"accuracy is :\",accuracy_score(ytest,ypred))\n",
    "cm=confusion_matrix(ytest,ypred)\n",
    "sns.heatmap(cm,annot=True)\n",
    "print(classification_report(ytest,ypred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning HyperParameters of Random Forest\n",
    "The most important parameter is the number of random features to sample at each split point (max_features).\n",
    "\n",
    "You could try a range of integer values, such as 1 to 20, or 1 to half the number of input features.\n",
    "\n",
    "    max_features [1 to 20]\n",
    "\n",
    "Alternately, you could try a suite of different default value calculators.\n",
    "\n",
    "    max_features in [‘sqrt’, ‘log2’]\n",
    "\n",
    "Another important parameter for random forest is the number of trees (n_estimators).\n",
    "\n",
    "Ideally, this should be increased until no further improvement is seen in the model.\n",
    "\n",
    "Good values might be a log scale from 10 to 1,000.\n",
    "\n",
    "    n_estimators in [10, 100, 1000]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model \n",
    "model=RandomForestClassifier()\n",
    "n_estimators =[10,50,100,1000]\n",
    "criterion =[\"gini\", \"entropy\"]\n",
    "max_features =[\"auto\", \"sqrt\", \"log2\"]\n",
    "#grid\n",
    "grid=dict(n_estimators=n_estimators,criterion=criterion,max_features=max_features)\n",
    "#cv\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "cv=RepeatedStratifiedKFold(n_splits=5,n_repeats=3,random_state=1)\n",
    "#GridSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "grid_cv=GridSearchCV(estimator=model,param_grid=grid,cv=cv,scoring='accuracy')\n",
    "#results\n",
    "res=grid_cv.fit(xtrain,ytrain)\n",
    "print(\"best parameters are :\",res.best_params_)\n",
    "print(\"best accuracy is :\",res.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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