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數(shù)據(jù)挖掘算法R語言實(shí)現(xiàn)之決策樹
2017-07-09
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數(shù)據(jù)挖掘算法R語言實(shí)現(xiàn)之決策樹

最近,看到很多朋友問我如何用數(shù)據(jù)挖掘算法R語言實(shí)現(xiàn)之決策樹,想要了解這方面的內(nèi)容如下:

> library("party")導(dǎo)入數(shù)據(jù)包
> str(iris) 集中展示數(shù)據(jù)文件的結(jié)構(gòu)
'data.frame': 150 obs. of 5 variables: 150條觀測(cè)值,5個(gè)變量
$ 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")

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