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 - Glo…