我正在使用 R 编程。
我将数据划分为训练和测试以预测准确性。
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)
以下错误信息发生在
confusionMatrix
:Error in confusionMatrix.default(rpartpred, test$Creditability) : the data cannot have more levels than the reference
信用数据可在本网站下载。
http://freakonometrics.free.fr/german_credit.csv
最佳答案
如果您仔细查看您的绘图,您会发现您正在训练回归树而不是分类树。
如果您运行 credit$Creditability <- as.factor(credit$Creditability)
读入数据后使用 type = "class"
在 predict 函数中,您的代码应该可以工作。
代码:
credit <- read.csv("http://freakonometrics.free.fr/german_credit.csv" )
credit$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)
关于r - 使用confusioMatrix时如何解决 "The data cannot have more levels than the reference"错误?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/38741997/