Learning Confidence Intervals for Feature Importance: A Fast Shapley-based Approach

Inferring feature importance is a well-known machine learning problem. Giving importance scores to the input data features is particularly helpful for explaining black-box models. Existing approaches rely on either statistical or Neural Network-based methods. Among them, Shapley Value estimates are among the mostly used scores to explain individual classification models or ensemble methods. As a drawback, state-of-the-art neural network-based approaches neglects the uncertainty of the input predictions while computing the confidence intervals of the feature importance scores. The paper extends a state-of-the-art neural method for Shapley Value estimation to handle uncertain predictions made by ensemble methods and to estimate a confidence interval for the feature importances. The results show that (1) The estimated confidence intervals are coherent with the expectation and more reliable than baseline methods; (2) The efficiency of the Shapley value estimator is comparable to those of traditional models; (3) The level of uncertainty of the Shapley value estimates decreases while producing ensembles of larger numbers of predictors.