Work

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

Year: 2021

Type: preprint

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 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2110.09697

Authors: Zhu, Jin, Wang, Xueqin, Hu, Liyuan, Huang, Junhao, Jiang, Kangkang, Zhang, Yanhang, Lin, Shiyun, Zhu, Junxian

Institutions: Sun Yat-sen University, National University of Singapore, University of Science and Technology of China, Renmin University of China, Peking University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Sun Yat-sen University, Renmin University of China, Fundamental Research Funds for the Central Universities

Awards: G1124736925, G1624938739, G418766553, G4549204025, G5318270844, G6346021793, G6848321182, G788615989

OpenAlex ID: W3206459514

Cites: 16

Cited by: 7

Related to: 10