Shap-E, short for Shapley Additive Explanations for Ensembles, is an extension of the SHAP framework tailored specifically for ensemble models. Ensembles, which combine multiple base models to improve predictive performance, are widely used in various domains. However, understanding the contributions and impact of individual models within an ensemble can be challenging. Shap-E aims to address this issue by providing interpretable explanations for ensemble predictions.

Shap-E builds upon the foundation of the SHAP framework, which utilizes game theory concepts to assign feature importance scores to individual input features. These scores represent the contribution of each feature towards a specific prediction. With Shap-E, the focus shifts to understanding the contributions of individual models within an ensemble. It leverages the principles of Shapley values, which allocate credits among the ensemble members based on their respective impact on the final prediction. By employing this approach, Shap-E provides explanations that highlight the relative importance of each model in the ensemble’s decision.