Learning an Interpretable Risk Scoring System for Maximizing Decision Net Benefit
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. The model is formulated as a sparse integer linear programming problem which enables the construction of a transparent scoring system with integer coefficients, and hence, facilitates interpretation and practical application. We also establish fundamental relationships among net benefit, discrimination, and calibra...
| Authors | Wenhao Chi, Ş. İlker Birbil |
|---|---|
| Primary category | cs.LG |
| Published | 5 April 2026 |
| Last updated at source | 2 October 2026 |
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