Model Interpretability

Interpreting ML Models

Understanding model predictions is crucial for trust.

Feature Importance

Tree-based: feature_importances_ show contributions. Permutation importance: shuffle feature, measure impact.

Global vs local importance: overall vs individual predictions.

SHAP Values

SHAP explains individual predictions. tree_explainer for tree models.

shap_values = explainer.shap_values(X). shap.summary_plot shows feature impacts.

LIME

Local interpretable model-agnostic explanations. Explains individual predictions.

LimeTabularExplainer for tabular data. Provides local linear approximation.

Key Takeaways

1. Feature importance shows global contributions
2. SHAP provides consistent feature attributions
3. LIME explains individual predictions locally