Can you explain the differences between SHAP and LIME?
Sure! SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are both techniques for interpreting machine learning models, but they have different approaches.
What are the main advantages of using SHAP over LIME?
SHAP provides consistent and theoretically grounded explanations based on cooperative game theory, while LIME focuses on local approximations.
Can you give me an example of how each method works?
Absolutely! For SHAP, you can visualize feature contributions for a specific prediction, while LIME generates a local linear model around the prediction to explain it.
Which one is more widely used in practice?
Both are popular, but SHAP is often preferred for its consistency and theoretical backing, especially in complex models.
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