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ML Model Explanation

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Interpret machine learning models using SHAP, LIME, feature importance, partial dependence, and attention visualization for explainability

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# 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
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