如何在 R 中做决策树？

Question

我通常在 SPSS 中做决策树以从 DDBB 获取目标，我做了一些研究，发现有三个包：tree、party 和 rpart 可用于 R，但哪个更好任务？

谢谢！

Answer 1

之前用过rpart，很好用。我通过拆分训练和测试集来进行预测建模。这是代码。希望这会给你一些想法......

 library(rpart)
    library(rattle)
    library(rpart.plot)
    ### Build the training/validate/test...

data(iris)
nobs <- nrow(iris) 
train <- sample(nrow(iris), 0.7*nobs)
test <- setdiff(seq_len(nrow(iris)), train)
colnames(iris)


### The following variable selections have been noted.
input <- c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width")
numeric <- c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width")
categoric <- NULL
target  <-"Species"
risk    <- NULL
ident   <- NULL
ignore  <- NULL
weights <- NULL

#set.seed(500)
# Build the Decision Tree model.
rpart <- rpart(Species~.,
    data=iris[train, ],
    method="class",
    parms=list(split="information"),
      control=rpart.control(minsplit=12,
        usesurrogate=0, 
        maxsurrogate=0))

# Generate a textual view of the Decision Tree model.
print(rpart)
printcp(rpart)

# Decision Tree Plot...
prp(rpart)
dev.new()
fancyRpartPlot(rpart, main="Decision Tree Graph")

如何在 R 中做决策树？

How to do decision trees in R?

r

party

rstudio

rpart