SHAP and LIME for Model Interpretability

In the enterprise AI landscape, model interpretability has become as crucial as model performance. SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are two powerful frameworks for making complex AI models more transparent and interpretable.

These tools provide complementary approaches to understanding model decisions: SHAP offers a game-theoretic approach to feature importance, while LIME creates locally interpretable approximations of complex models. Together, they provide a comprehensive toolkit for explaining model predictions and building trust in AI systems.

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