使用confusioMatrix时如何解决“数据不能有比参考更多的级别”错误？

我正在使用 R 编程。 我将数据分为训练数据和测试数据以预测准确性。

这是我的代码：

library("tree")
credit<-read.csv("C:/Users/Administrator/Desktop/german_credit (2).csv")

library("caret")
set.seed(1000)

intrain<-createDataPartition(y=credit$Creditability,p=0.7,list=FALSE)
train<-credit[intrain, ]
test<-credit[-intrain, ]

treemod<-tree(Creditability~. , data=train)
plot(treemod)
text(treemod)

cv.trees<-cv.tree(treemod,FUN=prune.tree)
plot(cv.trees)

prune.trees<-prune.tree(treemod,best=3)
plot(prune.trees)
text(prune.trees,pretty=0)

install.packages("e1071")
library("e1071")
treepred<-predict(prune.trees, newdata=test)

confusionMatrix(treepred, test$Creditability)

以下错误消息发生在confusionMatrix:

fusionMatrix.default(rpartpred, test$Creditability) 中的错误：数据的级别不能多于参考的级别

信用数据可以在此网站下载。
http://freakonometrics.free.fr/german_credit.csv http://freakonometrics.free.fr/german_credit.csv

如果仔细查看图，您会发现您正在训练回归树而不是分类树。

如果你跑credit$Creditability <- as.factor(credit$Creditability)读取数据并使用后type = "class"在预测函数中，您的代码应该可以工作。

code:

credit <- read.csv("http://freakonometrics.free.fr/german_credit.csv" )

credit$Creditability <- as.factor(credit$Creditability)

library(caret)
library(tree)
library(e1071)

set.seed(1000)
intrain <- createDataPartition(y = credit$Creditability, p = 0.7, list = FALSE)
train <- credit[intrain, ]
test <- credit[-intrain, ]

treemod <- tree(Creditability ~ ., data = train, )

cv.trees <- cv.tree(treemod, FUN = prune.tree)
plot(cv.trees)

prune.trees <- prune.tree(treemod, best = 3)
plot(prune.trees)
text(prune.trees, pretty = 0)

treepred <- predict(prune.trees, newdata = test, type = "class")
confusionMatrix(treepred, test$Creditability)