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

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  • Jongkwon Jo
  • Seungha Jung
  • Joongyang Park
  • Youngsoon Kim
  • Mingon Kang

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.

Suggested Citation

  • Jongkwon Jo & Seungha Jung & Joongyang Park & Youngsoon Kim & Mingon Kang, 2022. "Hi-LASSO: High-performance python and apache spark packages for feature selection with high-dimensional data," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-8, December.
    • Handle: RePEc:plo:pone00:0278570
    DOI: 10.1371/journal.pone.0278570
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    References listed on IDEAS

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    1. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    2. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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