L1 Lasso Regularization Embedded Feature Selection.ipynb

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    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.feature_selection import SelectFromModel"
   ]
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   "source": [
    "# Load the Iris dataset\n",
    "iris = load_iris()\n",
    "\n",
    "# Access the data and target attributes\n",
    "X = iris.data  # Features\n",
    "y = iris.target  # Target variable"
   ]
  },
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   "source": [
    "# Create Dataframe for both X and y\n",
    "X= pd.DataFrame(X, columns= iris.feature_names)\n",
    "y= pd.DataFrame(y, columns= ['target'])"
   ]
  },
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   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
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   "source": [
    "# Create a Lasso model\n",
    "lasso = Lasso(alpha=0.02, random_state=42)\n",
    "# 'alpha' controls the strength of the L1 regularization, higher values result in more feature sparsity"
   ]
  },
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   "source": [
    "# Create a feature selection transformer using SelectFromModel\n",
    "# This transformer will select features based on the model's coefficients\n",
    "feature_selector = SelectFromModel(lasso)"
   ]
  },
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SelectFromModel(estimator=Lasso(alpha=0.02, random_state=42))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectFromModel</label><div class=\"sk-toggleable__content\"><pre>SelectFromModel(estimator=Lasso(alpha=0.02, random_state=42))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.02, random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.02, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
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   "source": [
    "# Fit the transformer to the training data\n",
    "feature_selector.fit(X_train, y_train)"
   ]
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   "source": [
    "# Get the selected features using get_support\n",
    "selected_features = feature_selector.get_support(indices=True)"
   ]
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal length (cm)', 'petal length (cm)', 'petal width (cm)'], dtype='object')"
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   "source": [
    "# Get Selected Features Name (if data is in dataframe)\n",
    "selected_features_name= X_train.columns[selected_features]\n",
    "selected_features_name\n"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Now Transform Fitted Data in X_train and X_test\n",
    "X_train= feature_selector.transform(X_train)\n",
    "X_test= feature_selector.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Create New Dataframe with new selected features\n",
    "X_train_selected= pd.DataFrame(X_train, columns= selected_features)\n",
    "X_test_selected= pd.DataFrame(X_test, columns= selected_features)"
   ]
  },
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   "source": [
    "# Final Score After Feature Selection\n",
    "lasso.fit(X_train_selected,y_train)\n",
    "score= lasso.score(X_test_selected,y_test)\n",
    "score"
   ]
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