Fast Approximation of Shapley Values with Limited Data

Abstract

Shapley values have multiple desired and theoretically proven properties for explaining black-box model predictions. However, the exact computation of Shapley values can be computationally very expensive, precluding their use when timely explanations are required. FastSHAP is an approach for fast approximation of Shapley values using a trained neural network (the explainer). A novel approach, called FF-SHAP, is proposed, which incorporates three modifications to FastSHAP: i) the explainer is trained on ground-truth explanations rather than a weighted least squares characterization of the Shapley values, ii) cosine similarity is used as a loss function instead of mean-squared error, and iii) the actual prediction of the underlying model is given as input to the explainer. An empirical investigation is presented showing that FF-SHAP significantly outperforms FastSHAP with respect to fidelity, measured using both cosine similarity and Spearman's rank-order correlation. The investigation further shows that FF-SHAP even outperforms FastSHAP when using substantially smaller amounts of data to train the explainer, and more importantly, FF-SHAP still maintains the performance level of FastSHAP even when trained with as little as 15% of training data.