Learning Optimized Risk Scores

Understand

Risk scores are simple classification models that let users make quick risk predictions by adding and subtracting a few small numbers.

• These models are widely used in medicine and criminal justice, but are difficult to learn from data because they need to be calibrated, sparse, use small integer coefficients, and obey application-specific operational constraints.
• In this paper, we present a new machine learning approach to learn risk scores.
• We formulate the risk score problem as a mixed integer nonlinear program, and present a cutting plane algorithm for non-convex settings to efficiently recover its optimal solution.