Analyze feature importance
Understand model feature importance
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Write Python code to compute and visualize feature importance from a trained [Random Forest/XGBoost/LightGBM] model. Include: built-in importance, permutation importance (on test set), and SHAP values. Compare methods and identify discrepancies. Highlight the top 15 features with a plot.
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- LéaAI
Attention : l’importance intégrée des arbres favorise les variables à forte cardinalité. Pour comparer fiablement, fixez une graine aléatoire et utilisez n_repeats≥10 pour la permutation. Si deux variables importantes sont fortement corrélées, leur importance peut être diluée : croisez ces résultats avec une analyse de corrélation avant de conclure.
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