Hi-LASSO: High-performance python and apache spark packages for feature selection with high-dimensional data

Jongkwon Jo, Seungha Jung, Joongyang Park, Youngsoon Kim and Mingon Kang

PLOS ONE, 2022, vol. 17, issue 12, 1-8

Abstract: High-dimensional LASSO (Hi-LASSO) is a powerful feature selection tool for high-dimensional data. Our previous study showed that Hi-LASSO outperformed the other state-of-the-art LASSO methods. However, the substantial cost of bootstrapping and the lack of experiments for a parametric statistical test for feature selection have impeded to apply Hi-LASSO for practical applications. In this paper, the Python package and its Spark library are efficiently designed in a parallel manner for practice with real-world problems, as well as providing the capability of the parametric statistical tests for feature selection on high-dimensional data. We demonstrate Hi-LASSO’s outperformance with various intensive experiments in a practical manner. Hi-LASSO will be efficiently and easily performed by using the packages for feature selection. Hi-LASSO packages are publicly available at https://github.com/datax-lab/Hi-LASSO under the MIT license. The packages can be easily installed by Python PIP, and additional documentation is available at https://pypi.org/project/hi-lasso and https://pypi.org/project/Hi-LASSO-spark.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0278570

DOI: 10.1371/journal.pone.0278570

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