AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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Supersparse Linear Integer Models for Predictive Scoring Systems
Berk Ustun, Stefano Traca, Cynthia Rudin

Last modified: 2013-06-29


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.


Scoring Systems; Interpretability; Sparsity; Binary Classification; Supervised Learning

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