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