Supersparse Linear Integer Models for Predictive Scoring Systems
arXiv:1306.5860
Abstract
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.
Short version
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