Sparse regression over clusters: SparClur
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11590_2021_1770_ReferencePDF.pdf
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332.57 KB
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561e53200d4595a9aded0b9e2220f148
Author(s)
Bertsimas, Dimitris
Dunn, Jack
Kapelevich, Lea
Zhang, Rebecca
Date Issued
July 8, 2021
Publisher
Springer Berlin Heidelberg
Citation
Bertsimas, Dimitris, Dunn, Jack, Kapelevich, Lea and Zhang, Rebecca. 2021. "Sparse regression over clusters: SparClur."
Version
Author's final manuscript
Abstract
Abstract
Prediction tasks in personalized medicine require models that combine accuracy and interpretability. We propose an integer optimization approach for building sparse regression models with enforced coordination, using data partitioned among leaves in a prediction tree. We show that the method recovers the true underlying relationship between observations and target variables in large-scale synthetic data in seconds. We apply our method to several real-world medical prediction problems and observe that the additional structure imposed provides a substantial gain in interpretability, at a low cost to accuracy.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Operations Research Center
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/s11590-021-01770-9
