决策树R语言代码

最新推荐文章于 2024-05-20 20:30:45 发布

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最新推荐文章于 2024-05-20 20:30:45 发布

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分类专栏：数据挖掘

本文链接：https://blog.youkuaiyun.com/qq_42830971/article/details/107176164

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本文详细介绍使用R语言构建决策树模型的过程，包括数据读取、模型训练、复杂参数设置及模型剪枝，并展示了如何利用rpart和rpart.plot包进行决策树的可视化。

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讲解链接

R语言分类树，决策树
library(readxl)
data <- read_excel(path = 'D:\\sj1.xlsx')
library(rpart)
dtree<-rpart(事故责任~.,data=data,method="class", parms=list(split="information"))
printcp(dtree)
print(dtree)
tree<-prune(dtree,cp=0.0125)
tree<-prune(dtree,cp=dtree$cptable[which.min(dtree$cptable[,"xerror"]),"CP"])
opar<-par(no.readonly = T)
par(mfrow=c(1,1))
library(rpart.plot)
# #png(file = "D:\\tree1.png")
rpart.plot(dtree,
          type = 2, extra = "auto",
         under = FALSE, fallen.leaves = TRUE,       
         digits = 2, varlen = 0, faclen = 1, roundint = TRUE,
           cex = 0.6, tweak = 1,
           clip.facs = FALSE, clip.right.labs = TRUE,
           snip = FALSE,box.palette = 'red' ,shadow.col = 0,)
# #rpart.plot(dtree,branch=1,type=2, fallen.leaves=T,cex=0.8, sub="剪枝前")
# #png(file = "D:\\tree2.png")
# #rpart.plot(tree,branch=1, type=4,fallen.leaves=T,cex=0.8, sub="剪枝后")
# #par(opar)
# #dev.off()代码片

#R语言分类树，决策树
library(readxl)
data <- read_excel(path = 'D:\\个人文件\\研究生论文\\长三角\\数据整合\\data2019\\数据回归一张表201920210526.xlsx')
library(rpart)
dtree<-rpart(耦合协调度~Variable2+Variable8+Variable14+Variable16+Variable20+Variable22+Variable32+Variable34+Variable40+Variable52+Variable54+Variable64+Variable106+Variable115+Variable116+Variable82+Variable86,data=data,method="class", parms=list(split="information"))
printcp(dtree)

print(dtree)
tree<-prune(dtree,cp=0.0125)
tree<-prune(dtree,cp=dtree$cptable[which.min(dtree$cptable[,"xerror"]),"CP"])
opar<-par(no.readonly = T)
par(mfrow=c(1,1))
#library(rpart.plot)
# #png(file = "D:\\tree1.png")
rpart.plot(dtree,type = 2, extra = "auto",under = FALSE, fallen.leaves = TRUE,digits = 2, varlen = 0, faclen = 1, roundint = TRUE,cex = 0.6, tweak = 1,clip.facs = FALSE, clip.right.labs = TRUE,snip = FALSE,box.palette = 'red' ,shadow.col = 0,)
# #rpart.plot(dtree,branch=1,type=2, fallen.leaves=T,cex=0.8, sub="剪枝前")
# #png(file = "D:\\tree2.png")
# #rpart.plot(tree,branch=1, type=4,fallen.leaves=T,cex=0.8, sub="剪枝后")
# #par(opar)
# #dev.off()代码片