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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.16560 |
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| _version_ | 1866917147797618688 |
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| author | Schwendinger, Benjamin Schwendinger, Florian Vana-Gür, Laura |
| author_facet | Schwendinger, Benjamin Schwendinger, Florian Vana-Gür, Laura |
| contents | In this paper, we show how mixed-integer conic optimization can be used to combine feature subset selection with holistic generalized linear models to fully automate the model selection process. Concretely, we directly optimize for the Akaike and Bayesian information criteria while imposing constraints designed to deal with multicollinearity in the feature selection task. Specifically, we propose a novel pairwise correlation constraint that combines the sign coherence constraint with ideas from classical statistical models like Ridge regression and the OSCAR model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16560 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Model Selection for Generalized Linear Models Schwendinger, Benjamin Schwendinger, Florian Vana-Gür, Laura Machine Learning Optimization and Control 68T05 G.3; G.4 In this paper, we show how mixed-integer conic optimization can be used to combine feature subset selection with holistic generalized linear models to fully automate the model selection process. Concretely, we directly optimize for the Akaike and Bayesian information criteria while imposing constraints designed to deal with multicollinearity in the feature selection task. Specifically, we propose a novel pairwise correlation constraint that combines the sign coherence constraint with ideas from classical statistical models like Ridge regression and the OSCAR model. |
| title | Automated Model Selection for Generalized Linear Models |
| topic | Machine Learning Optimization and Control 68T05 G.3; G.4 |
| url | https://arxiv.org/abs/2404.16560 |