排列(音乐)
变量(数学)
特征选择
随机森林
树(集合论)
变量
计算
度量(数据仓库)
随机变量
计算机科学
计量经济学
数学
条件随机场
统计
机器学习
数据挖掘
算法
数学分析
物理
声学
作者
Carolin Strobl,Anne‐Laure Boulesteix,Thomas Kneib,Thomas Augustin,Achim Zeileis
标识
DOI:10.1186/1471-2105-9-307
摘要
Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been suggested as screening tools for, e.g., gene expression studies. However, these variable importance measures show a bias towards correlated predictor variables.We identify two mechanisms responsible for this finding: (i) A preference for the selection of correlated predictors in the tree building process and (ii) an additional advantage for correlated predictor variables induced by the unconditional permutation scheme that is employed in the computation of the variable importance measure. Based on these considerations we develop a new, conditional permutation scheme for the computation of the variable importance measure.The resulting conditional variable importance reflects the true impact of each predictor variable more reliably than the original marginal approach.
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