rpart 不在 R 中创建决策树，支持向量机工作

Question

我正在尝试创建用于分类的决策树，但未创建。使用 SVM（训练 == 测试数据）以 0.85 的精度执行相同的数据，"play" 是目标...

知道我做错了什么吗？这是数据和代码： https://gist.github.com/romeokienzler/c471819cbf156a69f73daf49f8c700c6

outlook,temp,humidity,windy,play

sunny,hot,high,false,no

sunny,hot,high,true,no

overcast,hot,high,false,yes

rainy,mild,high,false,yes

rainy,cool,normal,false,yes

rainy,cool,normal,true,no

overcast,cool,normal,true,yes

sunny,mild,high,false,no

sunny,cool,normal,false,yes

rainy,mild,normal,false,yes

sunny,mild,normal,true,yes

overcast,mild,high,true,yes

overcast,hot,normal,false,yes

rainy,mild,high,true,no

为了使用 SVM，我对数据进行了编码： https://gist.github.com/romeokienzler/9bfce4182eda3d7662315621462c9cc6

outlook,temp,humidity,windy,play

1,1,2,FALSE,FALSE

1,1,2,TRUE,FALSE

2,1,2,FALSE,TRUE

3,2,2,FALSE,TRUE

3,3,1,FALSE,TRUE

3,3,1,TRUE,FALSE

2,3,1,TRUE,TRUE

1,2,2,FALSE,FALSE

1,3,1,FALSE,TRUE

3,2,1,FALSE,TRUE

1,2,1,TRUE,TRUE

2,2,2,TRUE,TRUE

2,1,1,FALSE,TRUE

3,2,2,TRUE,FALSE

这是 SVM 案例：

library(e1071)
df= read.csv("5.tennis_encoded.csv")
attach(df)
x <- subset(df, select=-play)
y <- play
detach(df)
model = svm(x,y,type = "C")
pred = predict(model,x)
truthVector = pred == y
good = length(truthVector[truthVector==TRUE])
bad = length(truthVector[truthVector==FALSE])
good/(good+bad)

[1] 0.8571429

这是决策树

df= read.csv("5.tennis_encoded.csv")
library(rpart)
model = rpart(play ~ .,method = "class", data=df)
print(model)

1) 根 14 5 真 (0.3571429 0.6428571) *

所以我基本上得到了一棵只有根的树，游戏概率为 0.64% == 是

知道我做错了什么吗？

Answer 1

很可能您传递给算法的数据太少无法拆分。

查看 rpart.control 函数了解更多详情

rpart.control(minsplit = 20, minbucket = round(minsplit/3), cp = 0.01, 
              maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, xval = 10,
              surrogatestyle = 0, maxdepth = 30, ...)

如您所见，最小拆分大小为 20。

如果你

model = rpart(play ~ .,method = "class", data=df, control= rpart.control(minsplit=2))

你应该得到更多的分裂

rpart 不在 R 中创建决策树，支持向量机工作

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