When should I apply LIME instead of SHAP for explainability?

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When should I apply LIME instead of SHAP for explainability?

Asked on Nov 09, 2025

Answer

LIME and SHAP are both popular techniques for explainability in AI models, but they have different strengths and use cases. LIME is often preferred when you need a quick, local explanation for a specific prediction, especially in complex or black-box models, while SHAP provides a more comprehensive view of feature importance across the entire model.

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.

Additional Comment:

Use LIME when you need quick, instance-specific explanations.
Choose SHAP for a more comprehensive understanding of feature contributions across the model.
Consider computational resources and the need for local vs. global interpretability.
Both methods can be used in conjunction to provide a balanced view of model behavior.

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