Learning customized and optimized lists of rules with mathematical programming
Name
12532_2018_143_ReferencePDF.pdf
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962.84 KB
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Adobe PDF
Checksum (MD5)
2982b0149af963420957b83a20fc3d74
Author(s)
Rudin, Cynthia
Ertekin, Şeyda
Date Issued
September 5, 2018
Publisher
Springer Berlin Heidelberg
Version
Author's final manuscript
Abstract
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
.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s12532-018-0143-8
