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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统计机器学习
｜
211
30
，
min_samples_split
的变化范围设定为
20
到
100
。
scikit-learn
中的
GridSearchCV
方
法可以非常方便地在这些参数的所有组合上进行交叉验证，实现完全搜索，并通过交叉验
证模型的表现选出最优的参数集合。
6.2.5
预测连续的值
要使用树预测连续的值（也称为
回归
），遵循的逻辑和过程与前面基本相同，只是在测量
不纯度时，要基于每次分割时的均方偏差（均方误差），并通过均方根误差（
RMSE
）（
参
见
4.2.2
节）来判断预测效果。
scikit-learn
中的
sklearn.tree.DecisionTreeRegressor
方法可以训练决策树回归模型。
6.2.6
如何使用树
在组织中进行预测建模时，建模人员面临的一个主要困难是，他们所用的方法本质上是一
种“黑箱”，这使得建模方法很难与组织中的其他元素协同配合。从这个意义上说，树模
型具有两个非常突出的优点。
•
树模型提供了一种探索数据的可视化工具，它可以表示出哪个变量是重要的，以及变量
之间是如何关联的。树可以捕获预测变量之间的非线性关系。
•
树模型提供了一系列规则。可以与非专业人士有效地交流这些规则，这既有助于一个数
据挖掘项目的实现，也有助于将这个项目“卖”出去。
使用多棵树进行预测的效果通常比只用一棵树要好。特别地，随机森林和提升树算法几乎
总是能提供更高的预测准确度和更好的性能（参见
6.3
节与
6.4
节）
，但会丢失前面所说的
单棵树的优点。
本节要点
•
决策树生成一组规则来分类或预测一个结果。
• ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.