abess: A Fast Best Subset Selection Library in Python and R

• URL: http://arxiv.org/abs/2110.09697v1
• Date: Tue, 19 Oct 2021 02:34:55 GMT
• Title: abess: A Fast Best Subset Selection Library in Python and R
• Authors: Jin Zhu, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu, Xueqin Wang
• Abstract summary: We introduce a new library named abess that implements a unified framework of best-subset selection. The abess certifiably gets the optimal solution within times under the linear model. The core of the library is programmed in C++, and it can be installed from the Python library Index.
• Score: 1.6208003359512848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 under the linear model. Our efficient implementation allows abess to attain the solution of best-subset selection problems as fast as or even 100x 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.

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