如何在CART决策树算法中分割连续属性? [英] How to split continous attribute in CART decision tree algorithm?

问题描述

我不了解如何在CART(分类和回归树)算法中拆分连续属性，因为我们知道CART既可以拆分类别属性又可以拆分连续属性.

I don't understand about how to split continous attribute in CART (Classification and Regression Tree) algorithm, as we know that CART can both split categorical and continous attribute.

我读了许多论文，并说要分割点的值是顺序中的中间值.我不明白.您能给我解释一下这是什么意思，并举一些例子吗?

i have read many papers and it says the value to be split point is the middle value in sequence. i don't understand about it. could you explain to me what that means, and give me some examples?

谢谢

推荐答案

一般过程是扫描任何给定预测变量上的候选拆分值，测量每个拆分的质量并选择最佳拆分.为了提高效率，扫描可能不会尝试所有可能的拆分，而是尝试每个百分位数或其他一些简化的选择集.任何分割的质量都可以通过多种方式来衡量，例如信息增益，双向运动等.

The general process is to scan through candidate splitting values on any given predictor, measure the quality of each split and select the best one. For efficiency's sake, the scan may not try every possible split but instead try every percentile or some other reduced set of choices. The quality of any split can be measured any number of ways, such as information gain, twoing, etc.

如果您要专门谈论Breiman，Friedman，Stone Olshen最初描述的CART算法，请查看他们的书分类和回归树"(Classification and Regression Trees，1984).

If you are talking specifically about the CART algorithm originally described by Breiman, Friedman, Stone Olshen, then check their book, "Classification and Regression Trees" (1984).

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