Feature selection
Dimensionality reduction
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My dataset has [NUMBER] features to predict [TARGET_VARIABLE]. I want to select the most relevant [TARGET_NUMBER] features. Compare filter methods (correlation, chi2), wrapper methods (RFE), and embedded methods (Lasso, feature_importances). Implement the 3 approaches in Python and propose a consensus strategy.
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Attention : normalise tes features avant Lasso et RFE, et encode correctement les catégorielles pour chi2. Attention aussi au surapprentissage du RFE sur petit dataset : croise toujours la sélection en validation. Si les trois méthodes divergent, garde l’intersection des top-k plutôt que la moyenne des rangs.
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