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Updated: Jun 30, 2025

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双级变量选择方法的解释性

Gregor Buch1,2,3, Andreas Schulz1, Irene Schmidtmann2

  • 1Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.

Biometrical journal. Biometrische Zeitschrift
|March 23, 2024
PubMed
概括

双级变量选择方法优于标准LASSO,提高了模型的解释性,特别是在处理相关预测器时. 组指数 LASSO (GEL) 提供了一个平衡的方法来选择分组变量.

关键词:
双层次的选择选择.启动时可以使用bootstrapping.组选择变量组选择变量可以解释的解释性.

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