How to Solve "The Data Cannot Have More Levels Than the Reference" Error When Using confusioMatrix?
I'm Using R Programming. I Divided the Data as Train & Test for Predicting Accuracy. This Is My Code: Library("Tree") Credit
I'm using R programming. I divided the data as train & test for predicting accuracy.
This is my code:
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)
The following error message happens in confusionMatrix:
Error in confusionMatrix.default(rpartpred, test$Creditability) : the data cannot have more levels than the reference
The credit data can download at this site.
2 Answers
If you look carefully at your plots, you will see that you are training a regression tree and not a classication tree.
If you run credit$Creditability <- as.factor(credit$Creditability) after reading in the data and use type = "class" in the predict function, your code should work.
code:
credit <- read.csv("" )
credit$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)
I had the same issue in classification. It turns out that there is ZERO observation in a specific group therefore I got the error "the data cannot have more levels than the reference”.
Make sure there all groups in your test set appears in your training set.
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