Explainable AI: 5 Desired Frameworks in Python

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InterpretML: Simplifies complex models with model-agnostic interpretability tools

SHAP (Shapley Additive exPlanations): Provides unified and coherent explanations for model predictions

LIME (Local Interpretable Model-agnostic Explanations): Generates local approximations for better interpretability.

Anchor: Focuses on creating precise and understandable model explanations for individual predictions

AI Explainability 360: IBM's toolkit offers a comprehensive set of algorithms for explaining AI models in Python

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