AI RESEARCH
Learning An Interpretable Risk Scoring System for Maximizing Decision Net Benefit
arXiv CS.LG
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ArXi:2604.04241v1 Announce Type: new Risk scoring systems are widely used in high-stakes domains to assist decision-making. However, existing approaches often focus on optimizing predictive accuracy or likelihood-based criteria, which may not align with the main goal of maximizing utility. In this paper, we propose a novel risk scoring system that directly optimizes net benefit over a range of decision thresholds.