数据挖掘算法R语言实现之决策树

数据挖掘算法R语言实现之决策树

最近，看到很多朋友问我如何用数据挖掘算法R语言实现之决策树，想要了解这方面的内容如下：

>
library("party")导入数据包

> str(iris) 集中展示数据文件的结构

'data.frame': 150 obs. of 5 variables: 150条观测值，5个变量

$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...

$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...

$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5
...

$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1
...

$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1
1 1 1 1 1 ...

Call functionctreeto build a decision
tree. The first parameter is a formula, which defines a target
variable and a list of independent variables.

> iris_ctree <- ctree(Species ~
Sepal.Length Sepal.Width Petal.Length Petal.Width, data=iris)



> print(iris_ctree)



Conditional inference tree with 4 terminal nodes



Response: Species

Inputs: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width

Number of observations: 150



1) Petal.Length <= 1.9; criterion = 1, statistic =
140.264

2)* weights = 50

1) Petal.Length > 1.9

3) Petal.Width <= 1.7; criterion = 1, statistic =
67.894

4) Petal.Length <= 4.8; criterion = 0.999, statistic
= 13.865

5)* weights = 46

4) Petal.Length > 4.8

6)* weights = 8

3) Petal.Width > 1.7

7)* weights = 46



> plot(iris_ctree)



plot(iris_ctree, type="simple")