Model Explainability

LIME or SHAP? A Researcher's Decision Framework for Model Explainability

Computational scientists choosing between LIME and SHAP for model explainability must consider their specific needs regarding prediction scope, model architecture, and computational resources. This article provides a decision framework to help researchers select the most appropriate technique, weighing factors like local interpretability, global consistency, and efficiency trade-offs.