When should I apply LIME instead of SHAP for explainability?

Example Concept: LIME (Local Interpretable Model-agnostic Explanations) is useful for generating interpretable explanations for individual predictions by approximating the model locally around the instance of interest. It is particularly advantageous when you need to understand specific decision boundaries or when computational resources are limited. SHAP (SHapley Additive exPlanations), on the other hand, offers a unified measure of feature importance based on game theory, providing consistent and fair attributions across all predictions, which is ideal for global model interpretability.