Learning customized and optimized lists of rules with mathematical programming
Author(s)
Rudin, Cynthia; Ertekin, Şeyda
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Abstract
We introduce a mathematical programming approach to building rule lists, which are a type of interpretable, nonlinear, and logical machine learning classifier involving IF-THEN rules. Unlike traditional decision tree algorithms like CART and C5.0, this method does not use greedy splitting and pruning. Instead, it aims to fully optimize a combination of accuracy and sparsity, obeying user-defined constraints. This method is useful for producing non-black-box predictive models, and has the benefit of a clear user-defined tradeoff between training accuracy and sparsity. The flexible framework of mathematical programming allows users to create customized models with a provable guarantee of optimality. The software reviewed as part of this submission was given the DOI (Digital Object Identifier)
https://doi.org/10.5281/zenodo.1344142
.
Date issued
2018-09-05Department
Sloan School of ManagementPublisher
Springer Berlin Heidelberg