Error in Confusion Matrix : the data and reference factors must have the same number of levels
I've trained a Linear Regression model with R caret. I'm now trying to generate a confusion matrix and keep getting the following error:
Error in confusionMatrix.default(pred, testing$Final): the data and reference factors must have the same number of levels
EnglishMarks <- read.csv("E:/Subject Wise Data/EnglishMarks.csv",
header=TRUE)
inTrain<-createDataPartition(y=EnglishMarks$Final,p=0.7,list=FALSE)
training<-EnglishMarks[inTrain,]
testing<-EnglishMarks[-inTrain,]
predictionsTree <- predict(treeFit, testdata)
confusionMatrix(predictionsTree, testdata$catgeory)
modFit<-train(Final~UT1+UT2+HalfYearly+UT3+UT4,method="lm",data=training)
pred<-format(round(predict(modFit,testing)))
confusionMatrix(pred,testing$Final)
The error occurs when generating the confusion matrix. The levels are the same on both objects. I can’t figure out what the problem is. Their structure and levels are given below. They should be the same. Any help would be greatly appreciated as its making me cracked!!
> strpred)
chr[1:148] " 85"" 84"" 87"" 65" "88" "84" "82" "84" "65" "78" "78" "88" "85" "86" "77" ...
> str(testing$Final)
int [1:148] 88 85 86 70 85 85 79 85 62 77 ...
> levels(pred)
NULL
> levels(testing$Final)
NULL
Whenever you face an error: `data` and `reference` should be factors with the same levels, make sure that both the true values and the prediction values are of “factor” data-type.
Here both pred and testing$Final must be of datatype factor. Here testing$Final is of type int, convert it to factor and then build the confusion matrix.
confusionMatrix(factor(pred, levels=1:490), factor(testing$final, levels=1:490))
We have to keep in mind that both levels should be the same.
table(factor(pred, levels=min(test):max(test)), factor(test, levels=min(test):max(test)))// table is name the confusion matrix
It should give you exactly the same confusion matrix as with the function.
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