Model Agnostic Explainability

Choosing Between LIME, SHAP, and Permutation Importance: A Practical Guide to Model-Agnostic Explainability

This guide compares LIME, SHAP, and Permutation Importance, three model-agnostic techniques for explaining black-box models. It helps you choose the right method based on whether you need local or global explanations, computational budget, and data characteristics.