AI RESEARCH
RoSHAP: A Distributional Framework and Robust Metric for Stable Feature Attribution
arXiv CS.LG
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ArXi:2605.15154v1 Announce Type: cross Feature attribution analysis is critical for interpreting machine learning models and ing 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.