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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

1 Answer

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by (90.8k points)
edited by

Whenever you try to build a confusion matrix, 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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