Feature attribution analysis is critical for interpreting machine learning models and supporting reliable data-driven decisions. However, feature attribution measures often exhibit stochastic variation: different train--test splits, random seeds, or model-fitting procedures can produce substantially different attribution values and feature rankings. This paper proposes a framework for incorporating stochastic nature of feature attribution and a robust attribution metric, RoSHAP, for stable feature ranking based on the SHAP metric. The proposed framework models the distribution of feature attribution scores and estimates it through bootstrap resampling and kernel density estimation. We show that, under mild regularity conditions, the aggregated feature attribution score is asymptotically Gaussian, which greatly reduces the computational cost of distribution estimation. The RoSHAP summarizes the distribution of SHAP into a robust feature-ranking criterion that simultaneously rewards features that are active, strong, and stable. Through simulations and real-data experiments, the proposed framework and RoSHAP outperform standard single-run attribution measures in identifying signal features. In addition, models built using RoSHAP-selected features achieve predictive performance comparable to full-feature models while using substantially fewer predictors. The proposed RoSHAP approach improves the stability and interpretability of machine learning models, enabling reliable and consistent insights for analysis.

翻译：特征归因分析对于解释机器学习模型及支持可靠的数据驱动决策至关重要。然而，特征归因度量常呈现随机波动：不同的训练-测试划分、随机种子或模型拟合过程可能产生显著不同的归因值与特征排序。本文提出一种纳入特征归因随机性的框架，并基于SHAP度量设计了一种鲁棒归因度量RoSHAP，用于实现稳定的特征排序。所提框架对特征归因得分的分布进行建模，并通过Bootstrap重采样与核密度估计对其估计。我们证明，在温和正则条件下，聚合后的特征归因得分渐近服从高斯分布，这大幅降低了分布估计的计算成本。RoSHAP将SHAP分布总结为一种鲁棒特征排序准则，该准则同时奖励活跃、强效且稳定的特征。通过仿真与真实数据实验，所提框架与RoSHAP在识别信号特征方面优于标准单次归因度量。此外，基于RoSHAP所选特征构建的模型在显著减少预测变量数量的同时，可实现与全特征模型相当的预测性能。所提出的RoSHAP方法提升了机器学习模型的稳定性与可解释性，为分析提供了可靠且一致的洞见。