LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) have distinct strengths in explaining supervised ML model performance:

When comparing LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) for explaining supervised machine learning model performance, it's essential to consider their respective strengths and trade-offs. LIME focuses on providing local interpretations by approximating the model around a specific prediction, making it useful for understanding individual predictions. Its model-agnostic nature allows it to be applied to any machine learning model, and it is relatively simple to implement. However, LIME's explanations may lack stability due to its reliance on local approximations, and generating explanations for each prediction can be computationally intensive. On the other hand, SHAP values, based on Shapley values from cooperative game theory, offer consistent and accurate explanations. SHAP provides both global feature importance and local explanations, ensuring fairness in feature attribution. However, SHAP's complexity and computational expense may pose challenges for implementation and understanding, particularly for non-technical stakeholders. Ultimately, the choice between LIME and SHAP depends on the specific requirements of the task, with LIME offering simplicity and quick local explanations and SHAP providing robust, theoretically grounded interpretations with global and local interpretability.

TL;DR: The choice between these two methods depends on specific requirements. As a rule of thumb, LIME is simpler and faster, while SHAP is more robust but a more complex method to understand, hence providing lower explainability of models.

Long Answer:

Both SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) are techniques used to understand how a machine learning model arrives at its predictions. Let's break down their functionalities and evolution and compare them using a table and formulas.

SHAP uses game theory to explain feature contributions. It calculates Shapley values, which represent a feature's fair share of the prediction by considering all possible feature combinations. SHAP provides both local (for individual instances) and global explanations (for overall model behavior).

LIME works by approximating a complex model locally around a specific prediction. It creates a simpler, interpretable model (usually a linear model) in the vicinity of the instance and analyzes how features influence the prediction in that local region. LIME excels at explaining individual predictions.

A more advanced method would be Tree SHAP, which was introduced much later than these two and usually provides better results.

Thanks Mohammed Saha Alam , Zimam Ahamed , Arindra Nath Mishra