  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.
