如何解决“数据不能具有比参考更多的级别”?使用confusioMatrix时出错? [英] How to solve "The data cannot have more levels than the reference" error when using confusioMatrix?
问题描述
我正在使用R编程。
我将数据除以train&
I'm using R programming. I divided the data as train & test for predicting accuracy.
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)
在 confusionMatrix
中发生以下错误消息:
The following error message happens in confusionMatrix
:
confusionMatr错误ix.default(rpartpred,test $ Creditability):数据的级别不能超过参考
Error in confusionMatrix.default(rpartpred, test$Creditability) : the data cannot have more levels than the reference
信用数据可以在此处下载网站。
http://freakonometrics.free.fr/german_credit.csv
The credit data can download at this site.
http://freakonometrics.free.fr/german_credit.csv
推荐答案
如果仔细查看情节,您会看到您正在训练的是回归树,而不是经典树。
If you look carefully at your plots, you will see that you are training a regression tree and not a classication tree.
如果在读取数据后运行 credit $ Creditability<-as.factor(credit $ Creditability)
并在预测函数中使用 type = class
,您的代码应该可以使用。
If you run credit$Creditability <- as.factor(credit$Creditability)
after reading in the data and use type = "class"
in the predict function, your code should work.
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)
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