Feature Engineering, Feature Selection, and Preventing Data Leakage (Chapter 3.4)

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Feature engineering and feature selection are critical processes in building effective machine learning workflows. This article explores interaction terms, Recursive Feature Elimination (RFE), Lasso L1 regularization, and leak-proof Scikit-Learn pipelines.1. Feature Engineering: Interaction Terms and PolynomialsCreating polynomial features and interaction terms allows linear models to capture non-linear relationships and complex feature interactions.from sklearn.preprocessing import PolynomialFeatures poly = PolynomialFeatures(degree=2, interaction_only=True) X_poly = poly.fit_transform(X)2. Feature Selection MethodsRecursive Feature Elimination (RFE)RFE greedily removes the least important features recursively until the desired number of features is reached.from sklearn.feature_selection import RFE from sklearn.linear_model import LogisticRegression selector = RFE(estimator=LogisticRegression(), n_features_to_select=5) X_selected = selector.fit_transform(X, y)Lasso L1 RegularizationLasso penalty forces coefficients of non-essential features to zero, effectively performing feature selection.from sklearn.linear_model import LassoCV lasso = LassoCV().fit(X, y) selected_features = X.columns[lasso.coef_ != 0]3. Preventing Data Leakage

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