标题： 二元回归树和森林中的变量重要性 显示英文标题

标题： Variable importance in binary regression trees and forests

摘要： 我们刻画并研究了二元回归树中的变量重要性（VIMP）和变量两两关联。 一个关键组成部分涉及我们称为最大子树的量的节点均方误差。 该理论自然地从单棵树扩展到树的集成，并适用于随机森林等方法。 这是有用的，因为尽管随机森林的重要性值用于筛选变量（例如，它们用于生物信息学中过滤高通量基因组数据），但关于其性质的理论却很少。 显示英文摘要

摘要： We characterize and study variable importance (VIMP) and pairwise variable associations in binary regression trees. A key component involves the node mean squared error for a quantity we refer to as a maximal subtree. The theory naturally extends from single trees to ensembles of trees and applies to methods like random forests. This is useful because while importance values from random forests are used to screen variables, for example they are used to filter high throughput genomic data in Bioinformatics, very little theory exists about their properties.