What are the differences between SHAP and LIME?

What are the differences between SHAP and LIME?

SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are both techniques for interpreting machine learning models. SHAP provides a unified measure of feature importance based on game theory, while LIME focuses on local approximations of the model to explain individual predictions.

Which one is better for model interpretability?

It depends on your needs. SHAP offers a more consistent and theoretically grounded approach, while LIME is often easier to implement and understand for specific predictions.

Can you give an example of when to use each?

Use SHAP when you need a global understanding of feature importance across the entire model. Use LIME when you want to explain a specific prediction in detail.

Are there any limitations to these methods?

Yes, SHAP can be computationally intensive, especially for large datasets, while LIME may produce unstable explanations if the model is complex.