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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220
｜
第
6
章
图
6-8
中的上图和下图分别给出了按照准确度下降和按照
Gini
不纯度下降的变量重要
性，其中变量都是按照准确度下降来排序的，这两种测量方式得到的变量重要性分数相
差非常大。
既然准确度下降是一种更加可靠的测量方式，为什么还要使用
Gini
不纯度下降这种方法
呢？默认情况下，
randomForest
只计算
Gini
不纯度：
Gini
不纯度是算法附带的一种结果，
而各个变量的模型准确度需要额外的计算（需要对数据进行随机重排并预测这种数据）。
在计算复杂性比较重要的情况下，比如需要拟合几千个模型的生产情况，可能不值得进行
额外的计算工作。此外，
Gini
不纯度下降方法还可以说明随机森林使用了哪个变量来组成
它的分割规则（回忆一下，这种在简单树中显而易见的规则在随机森林中实际上丢失了）。
6.3.4
超参数
与很多统计机器学习算法一样，随机森林也被认为是一种“黑箱”算法。有一些参数可以
用来调整该算法的工作方式，这些参数称为
超参数
（
hyperparameter
）
，就是需要在拟合模
型之前设定的参数，它们不是在训练过程中进行优化的。在传递的统计模型中，需要做一
些选择（比如选择在回归模型中使用的预测变量），但随机森林超参数的作用更加重要，
特别是为了避免过拟合。特别需要提及的是，随机森林中有两个最为重要的超参数。
nodesize/min_samples_leaf
终端节点（树的叶子节点）的最小体积。在
R
中，对于分类问题，它的默认值是
1
；对
于回归问题，它的默认值是
5
。在
Python
的
scikit-learn
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