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
｜
205
树模型就是一组“
if-then-else
”规则的集合，很好理解，实现也非常简单。与线性回归和
逻辑回归相比，树具有发现数据中隐藏模式的能力，这些模式对应于数据中复杂的交互作
用。与
KNN
或朴素贝叶斯方法不同
，简单的树模型可以用预测变量之间的关系表达出来，
这非常易于解释。
运筹学中的决策树
在决策科学与运筹学中，
决策树
这个词有另一种（更加古老的）意义，它表
示的是一种人工的决策分析过程。在这个意义中，决策点、可能结果以及它
们的估计概率都被放在一张分支图中，并选择期望值最大的决策路径。
6.2.1
一个简单的例子
R
中拟合树模型的两个主要的包是
rpart
和
tree
。下面使用
rpart
包，在有
3000
条记录的
贷款数据样本上拟合一个模型，使用的变量是
payment_inc_ratio
和
borrower_score
（参
见
6.1
节中的数据描述）
。
library(rpart)
loan_tree <- rpart(outcome ~ borrower_score + payment_inc_ratio,
data=loan3000, control=rpart.control(cp=0.005))
plot(loan_tree, uniform=TRUE, margin=0.05)
text(loan_tree)
sklearn.tree.DecisionTreeClassifier
也提供了一种决策树实现。
dmba
包中还有一个非常
方便的函数，可以在
Jupyter
笔记本中创建决策树的可视化表示。 ...
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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.