abess swMATH ID: 40395 Software Authors: Jin Zhu, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu, Xueqin Wang Description: abess: Fast Best-Subset Selection in Python and R. abess (Adaptive BEst Subset Selection) library aims to solve general best subset selection, i.e., find a small subset of predictors such that the resulting model is expected to have the highest accuracy. The selection for best subset shows great value in scientific researches and practical applications. For example, clinicians want to know whether a patient is healthy or not based on the expression levels of a few of important genes. This library implements a generic algorithm framework to find the optimal solution in an extremely fast way. This framework now supports the detection of best subset under: linear regression, classification (binary or multi-class), counting-response modeling, censored-response modeling, multi-response modeling (multi-tasks learning), etc. It also supports the variants of best subset selection like group best subset selection, nuisance penalized regression, Especially, the time complexity of (group) best subset selection for linear regression is certifiably polynomial Homepage: https://abess.readthedocs.io/en/latest/ Source Code: https://github.com/abess-team/abess Dependencies: Python; R Keywords: Machine Learning; arXiv_stat.ML; arXiv_cs.LG; arXiv_stat.CO; Adaptive BEst Subset Selection; Python; R; Best-Subset Selection; High Dimensional Data; Splicing Technique Related Software: scikit-survival; mlr3; OpenML-Python; UCI-ml; Glmulti; mlr3pipelines; Scikit; R; Python Cited in: 0 Documents Standard Articles 1 Publication describing the Software Year abess: A Fast Best Subset Selection Library in Python and R Jin Zhu, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu, Xueqin Wang 2021