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The SHAP framework provides a principled method to explain the predictions ofa model by computing feature importance. Motivated by applications in finance,we introduce the Top-k Identification Problem (TkIP), where the objective is toidentify the k features with the highest SHAP values. While any method tocompute SHAP values with uncertainty estimates (such as KernelSHAP andSamplingSHAP) can be trivially adapted to solve TkIP, doing so is highly sampleinefficient. The goal of our work is to improve the sample efficiency ofexisting methods in the context of solving TkIP. Our key insight is that TkIPcan be framed as an Explore-m problem--a well-studied problem related tomulti-armed bandits (MAB). This connection enables us to improve sampleefficiency by leveraging two techniques from the MAB literature: (1) a betterstopping-condition (to stop sampling) that identifies when PAC (ProbablyApproximately Correct) guarantees have been met and (2) a greedy samplingscheme that judiciously allocates samples between different features. Byadopting these methods we develop KernelSHAP@k and SamplingSHAP@k toefficiently solve TkIP, offering an average improvement of $5\times$ insample-efficiency and runtime across most common credit related datasets.

SHAP@k:Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features