predictive scoring system
Supersparse Linear Integer Models for Predictive Scoring Systems
Ustun, Berk (Massachusetts Institute of Technology) | Traca, Stefano (Massachusetts Institute of Technology) | Rudin, Cynthia (Massachusetts Institute of Technology)
We introduce Supersparse Linear Integer Models (SLIM) as a tool to create data-driven scoring systems for binary classification. We derive theoretical bounds on the true risk of SLIM scoring systems, and present experimental results to show that SLIM scoring systems are accurate, sparse, and interpretable classification models.
Supersparse Linear Integer Models for Predictive Scoring Systems
Ustun, Berk, Traca, Stefano, Rudin, Cynthia
We introduce Supersparse Linear Integer Models (SLIM) as a tool to create scoring systems for binary classification. We derive theoretical bounds on the true risk of SLIM scoring systems, and present experimental results to show that SLIM scoring systems are accurate, sparse, and interpretable classification models.