ML Model Explanation

SKILL.md

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