SHAP Interpretability
Explaining model predictions
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I trained a [MODEL_TYPE] to predict [TARGET_VARIABLE] using [NUMBER] features. Explain how to use SHAP to interpret this model: computing SHAP values, visualizations to create (summary plot, dependence plot, force plot), and how to communicate these results to non-technical stakeholders.
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Pour accélérer les calculs sur de gros jeux de données, utilisez `shap.sample()` ou `KernelExplainer` sur un échantillon représentatif. Si votre modèle est basé sur des arbres, préférez `TreeExplainer` – jusqu’à 100x plus rapide et exact. Pour les stakeholders, un force plot sur un cas concret parle plus qu’un summary plot abstrait.
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