Type I error control for tree classification.
Type I error control for tree classification.
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作者：
Jung SH;Chen Y;Ahn H
二叉树分类对于根据与所选择的预测者相关的结果变量的水平对整个人群进行分类是有用的。通常，我们从大量的候选预测值开始分类，并且每个预测值都有许多不同的分界值。由于这些类型的多重性，二叉树分类方法容易受到严重的I类错误概率的影响。尽管如此，涉及这一问题的出版物并不多。在本文中，我们提出了一种二叉树分类方法，以控制接受某个预测值的概率低于一定的水平，例如5%。
Binary tree classification has been useful for classifying the whole population based on the levels of outcome variable that is associated with chosen predictors. Often we start a classification with a large number of candidate predictors, and each predictor takes a number of different cutoff values. Because of these types of multiplicity, binary tree classification method is subject to severe type I error probability. Nonetheless, there have not been many publications to address this issue. In this paper, we propose a binary tree classification method to control the probability to accept a predictor below certain level, say 5%.