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

翻译：SHAP框架通过计算特征重要性提供了一种解释模型预测的原则性方法。受金融领域应用启发，我们提出Top-k识别问题（TkIP），其目标是识别具有最高SHAP值的k个特征。虽然任何能够计算带不确定性估计的SHAP值的方法（如KernelSHAP和SamplingSHAP）均可简单适配以解决TkIP，但这样做会导致样本效率极低。我们工作的目标是在解决TkIP的背景下提升现有方法的样本效率。我们的关键见解在于：TkIP可被形式化为一个Explore-m问题——一个与多臂老虎机（MAB）密切相关且经过充分研究的问题。这一关联使我们能够利用MAB文献中的两种技术提升样本效率：（1）更优的停止条件（用于停止采样），该条件可识别何时满足概率近似正确（PAC）保证；（2）一种在各特征间审慎分配样本的贪婪采样方案。通过采用这些方法，我们开发了KernelSHAP@k与SamplingSHAP@k以高效求解TkIP，在大多数常见信贷相关数据集上平均实现了5倍的样本效率与运行时间提升。