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Supersparse Linear Integer Models for Predictive Scoring Systems
Published 25 Jun 2013 in stat.ML | (1306.5860v1)
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.
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