abess: A Fast Best Subset Selection Library in Python and R
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
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| _version_ | 1866909155026010112 |
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| author | Zhu, Jin Wang, Xueqin Hu, Liyuan Huang, Junhao Jiang, Kangkang Zhang, Yanhang Lin, Shiyun Zhu, Junxian |
| author_facet | Zhu, Jin Wang, Xueqin Hu, Liyuan Huang, Junhao Jiang, Kangkang Zhang, Yanhang Lin, Shiyun Zhu, Junxian |
| contents | We introduce a new library named abess that implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classification, and principal component analysis. Particularly, the abess certifiably gets the optimal solution within polynomial times with high probability under the linear model. Our efficient implementation allows abess to attain the solution of best-subset selection problems as fast as or even 20x faster than existing competing variable (model) selection toolboxes. Furthermore, it supports common variants like best group subset selection and $\ell_2$ regularized best-subset selection. The core of the library is programmed in C++. For ease of use, a Python library is designed for conveniently integrating with scikit-learn, and it can be installed from the Python library Index. In addition, a user-friendly R library is available at the Comprehensive R Archive Network. The source code is available at: https://github.com/abess-team/abess. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2110_09697 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | abess: A Fast Best Subset Selection Library in Python and R Zhu, Jin Wang, Xueqin Hu, Liyuan Huang, Junhao Jiang, Kangkang Zhang, Yanhang Lin, Shiyun Zhu, Junxian Machine Learning Computation We introduce a new library named abess that implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classification, and principal component analysis. Particularly, the abess certifiably gets the optimal solution within polynomial times with high probability under the linear model. Our efficient implementation allows abess to attain the solution of best-subset selection problems as fast as or even 20x faster than existing competing variable (model) selection toolboxes. Furthermore, it supports common variants like best group subset selection and $\ell_2$ regularized best-subset selection. The core of the library is programmed in C++. For ease of use, a Python library is designed for conveniently integrating with scikit-learn, and it can be installed from the Python library Index. In addition, a user-friendly R library is available at the Comprehensive R Archive Network. The source code is available at: https://github.com/abess-team/abess. |
| title | abess: A Fast Best Subset Selection Library in Python and R |
| topic | Machine Learning Computation |
| url | https://arxiv.org/abs/2110.09697 |