Web: http://arxiv.org/abs/2110.09697

June 20, 2022, 1:12 a.m. | Jin Zhu, Xueqin Wang, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu

stat.ML updates on arXiv.org arxiv.org

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 …

arxiv library ml python

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