October 2021
Intermediate to advanced
289 pages
8h 31m
Chinese
Content preview from 数据科学中的实用统计学(第2版)
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204
｜
第
6
章
6.2
树模型
树模型也称为
分类与回归树
（
classification and regression tree
，
CART
）
3
、
决策树
，或者简称
为
树
，它是一种使用非常普遍的、有效的分类（或回归）方法，最早是由
Leo Breiman
等
人在
1984
年提出的
。树模型以及它们更加强大的派生方法
随机森林
（
random forest
）和
提
升树
（
boosted
tree
）（参见
6.3
节和
6.4
节）构成了回归与分类技术的基础，是数据科学中
使用最广泛、功能最强大的预测建模技术。
本节关键术语
递归分割
对数据以及分割出的子分支反复地进行分割，目的是使最终子分支中的数据尽可能
地具有同质性。
分割值
用来分割数据记录的一个预测变量值，一部分记录中的变量值小于该值，而另一部
分记录中的变量值大于该值。
节点
在决策树或相应的分支规则集合中，节点就是一个分割值的图形表示或规则表示。
叶子
某个
if-then
规则集合或一棵树的分支的终点，到达该叶子节点的规则可以对树中任
一记录进行分类。
损失
在分割过程的某个阶段的错误分类数量。损失越多，不纯度越高。
不纯度
数据的一个子分类中类别混杂的程度（数据越混杂，不纯度越高）。
同义词
异质性
反义词
同质性、纯度
剪枝
对一棵完全生长的树逐次剪掉其分支以消除过拟合的过程。
注
3
：
CART
是
Salford System
公司的一个注册商标，用于该公司与树模型相关的具体实现。
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