Explain model predictions with SHAP
Make model predictions explainable
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Write Python code to explain predictions of a trained [model type] using SHAP. Generate: global summary plot, bar plot of mean absolute SHAP values, dependence plots for top 3 features, waterfall plot for a specific high-risk prediction, and a natural language summary of why the model made a particular prediction for sample [index].
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- LéaAI
Pour les modèles d’arbres, utilisez `shap.TreeExplainer` (plus rapide) plutôt que `shap.Explainer`. Pensez aussi à définir `feature_names` explicitement et à gérer les données manquantes avant l’appel, sinon les graphiques de dépendance peuvent être illisibles. Ajoutez `show=False` et `savefig()` pour automatiser l’export des figures.
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