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stat.ML2013
A Statistical Learning Theory Framework for Supervised Pattern Discovery
Jonathan H. Huggins, Cynthia Rudin
This paper formalizes a latent variable inference problem we call {\em supervised pattern discovery}, the goal of which is to find sets of observations that belong to a single ``pa…
stat.ML2013★ 8 cited
Supersparse Linear Integer Models for Predictive Scoring Systems
Berk Ustun, Stefano Traca, Cynthia Rudin
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 s…
stat.ML2013★ 27 cited
Supersparse Linear Integer Models for Interpretable Classification
Berk Ustun, Stefano Tracà, Cynthia Rudin
Scoring systems are classification models that only require users to add, subtract and multiply a few meaningful numbers to make a prediction. These models are often used because t…