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Updated: May 23, 2025

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森林随机变量选择方法的比较,用于连续结果的回归建模

Nathaniel S O'Connell1, Byron C Jaeger1, Garrett S Bullock1,2

  • 1Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, United States.

Briefings in bioinformatics
|March 10, 2025
PubMed
概括

本研究对13种随机森林 (RF) 变量选择方法进行了基准测试,以获得连续结果. 推Boruta和aorsf R包用于基于轴的射频模型,而aorsf是最适合斜射频模型的.

关键词:
功能减少 功能减少功能选择 功能选择随机的森林随机的森林这是一个回归回归的回归.选择变量的选择变量.

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