Sparse regression over clusters: SparClur
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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Berlin Heidelberg
2022
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| Online Access: | https://hdl.handle.net/1721.1/140530 |
| _version_ | 1826209533006970880 |
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| author | Bertsimas, Dimitris Dunn, Jack Kapelevich, Lea Zhang, Rebecca |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Bertsimas, Dimitris Dunn, Jack Kapelevich, Lea Zhang, Rebecca |
| author_sort | Bertsimas, Dimitris |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:23:58Z |
| format | Article |
| id | mit-1721.1/140530 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:23:58Z |
| publishDate | 2022 |
| publisher | Springer Berlin Heidelberg |
| record_format | dspace |
| spelling | mit-1721.1/1405302023-02-06T20:43:29Z Sparse regression over clusters: SparClur Bertsimas, Dimitris Dunn, Jack Kapelevich, Lea Zhang, Rebecca Sloan School of Management Massachusetts Institute of Technology. Operations Research Center 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. 2022-02-18T16:25:26Z 2022-02-18T16:25:26Z 2021-07-08 2022-02-17T04:18:16Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/140530 Bertsimas, Dimitris, Dunn, Jack, Kapelevich, Lea and Zhang, Rebecca. 2021. "Sparse regression over clusters: SparClur." en https://doi.org/10.1007/s11590-021-01770-9 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature application/pdf Springer Berlin Heidelberg Springer Berlin Heidelberg |
| spellingShingle | Bertsimas, Dimitris Dunn, Jack Kapelevich, Lea Zhang, Rebecca Sparse regression over clusters: SparClur |
| title | Sparse regression over clusters: SparClur |
| title_full | Sparse regression over clusters: SparClur |
| title_fullStr | Sparse regression over clusters: SparClur |
| title_full_unstemmed | Sparse regression over clusters: SparClur |
| title_short | Sparse regression over clusters: SparClur |
| title_sort | sparse regression over clusters sparclur |
| url | https://hdl.handle.net/1721.1/140530 |
| work_keys_str_mv | AT bertsimasdimitris sparseregressionoverclusterssparclur AT dunnjack sparseregressionoverclusterssparclur AT kapelevichlea sparseregressionoverclusterssparclur AT zhangrebecca sparseregressionoverclusterssparclur |