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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
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PubMed
概括
此摘要是机器生成的。

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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科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 生物信息学是一种生物信息学.

背景情况:

  • 变量选择通过创建较少的模型来增强模型的解释性.
  • 标准的以稀疏度为重点的方法可能会失败,当预测因素是相关的或上下文相关.
  • 双级选择可以在特征组内识别预测成员.

研究的目的:

  • 调查双级变量选择技术是否与标准LASSO相比提高了模型的解释性.
  • 评估对LASSO的组指数LASSO (GEL),稀疏组LASSO (SGL) 和复合最小形惩罚 (cMCP) 的性能.
  • 在不同的分组策略下评估选择相关性,组一致性和对线性容忍度.

主要方法:

  • 应用了GEL,SGL,cMCP和LASSO在时间到事件,回归和分类任务中进行预测器选择.
  • 使用来自1001名患者队列的引导样本.
  • 使用基于先前知识,相关性和随机分配的分组进行比较的方法.

主要成果:

  • 双级选择方法在所有评估标准中始终优于LASSO.
  • cMCP显示出优越的选择相关性.
  • SGL 显示了强大的群体一致性.
  • 盖尔展现了全方位的能力,选择了高度相关的相关和相关的预测因素.

结论:

  • 对于具有分组或相关变量的可解释模型,双级选择方法比 LASSO 更有效.
  • 特别推GEL是因为它能够联合选择相关预测因素,同时保持高可解释性.
  • 选择分组策略会影响双层选择方法的性能.