Interpret machine learning models using SHAP, LIME, feature importance, partial dependence, and attention visualization for explainability
ML Model Explanation Model explainability makes machine learning decisions transparent and interpretable, enabling trust, compliance, debugging, and actionable insights from predictions. Explanation Techniques Feature Importance: Global feature contribution to predictions SHAP Values: Game theory-based feature attribution LIME: Local linear approximations for individual predictions Partial Dependence Plots: Feature relationship with predictions Attention Maps: Visualization of model focus areas Surrogate Models: Simpler interpretable approximations Explainability Types Global: Overall model behavior and patterns Local: Explanation for individual predictions Feature-Level: Which features matter most Model-Level: How different components interact
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