Differences between LIME and SHAP in XAI

As artificial intelligence (AI) systems become more widespread and impactful, there’s a growing need to understand how these systems make decisions. This demand for transparency has led to the development of Explainable AI (XAI) — a field dedicated to making machine learning models more interpretable.

Two popular tools used in XAI are LIME and SHAP. They help data scientists and developers explain complex black-box models in simple, human-understandable terms.

What is LIME?

LIME stands for Local Interpretable Model-agnostic Explanations. It is a technique designed to explain the predictions of any machine learning model by approximating it locally with an interpretable model. LIME works by perturbing the input data slightly and observing how the predictions change. It then uses this information to train a simple, interpretable model (like linear regression) around the specific prediction point. This local model is easier for humans to understand and gives insights into why the original model made a certain decision.

What is SHAP?

SHAP stands for SHapley Additive exPlanations. It is based on game theory, specifically Shapley values, which determine the contribution of each feature to a prediction. SHAP provides both local and global interpretability by fairly distributing the “credit” or importance among the input features. It has strong theoretical guarantees and consistency, making it one of the most reliable tools for interpreting complex models like deep learning or gradient boosting machines.

LIME vs SHAP

	LIME	SHAP
Full Form	Local Interpretable Model-agnostic Explanations	SHapley Additive exPlanations
Foundation	Uses local surrogate models	Based on Shapley values from game theory
Interpretability	Local explanations only	Both local and global explanations
Consistency	Not always consistent	Theoretically guaranteed consistency
Speed	Faster and more lightweight	Can be slower due to complex computation
Model-Agnostic	Yes	Yes, but some variants (like TreeSHAP) are model-specific
Visualization	Simple and intuitive visualizations	Rich and detailed visualizations with feature importance

 

Both LIME and SHAP are powerful tools that serve the core goal of Explainable AI: helping humans understand and trust machine learning models. Choosing between them depends on the specific use case, model complexity, and interpretability needs.