train() function in r - SVM - r

Question

I'm running a SVM in R with caret package. My entire df (named total, which includes train and test) are scaled numbers from 0 to 1. My Y is binary (0-1). All the variables have the class "num". Here is the code:
model_SVM <- train(
Y ~ ., training,
method = "svmPoly",
trControl = trainControl(
method = "cv", number = 10,
verboseIter = TRUE
)
)
summary(model_SVM)
model_SVM
SVMprediction <-predict(model_SVM, testing)
cmSVM <-confusionMatrix(SVMprediction,testing$Y) # ERROR
print(SVMprediction)
I got this error1 in the line # ERROR
> cmSVM <-confusionMatrix(SVMprediction,testing$Y)
Error: `data` and `reference` should be factors with the same levels.
It was solved by adding:
SVMprediction<-as.factor(SVMprediction)
testing$Y<-as.factor(testing$Y)
However, I got an error2 now in # ERROR2:
Error in confusionMatrix.default(SVMprediction, testing$Y) :
the data cannot have more levels than the reference
When I check the levels, SVMprediction has 361 levels, and testing$Y 2 levels. How SVMprediction got 361 levels if the Y had just two?
Thanks!
PS: The full code:
totalY <- total
total <- total%>%
select(-Y)
# Missing Values with MICE
mod_mice <- mice(data = total, m = 5,meth='cart')
total <- complete(mod_mice)
post_mv_var_top10 <- total
Y <- totalY$Y
total<-cbind(total,Y)
train_ <- total%>%
filter(is.na(Y)==FALSE)
test_ <- total%>%
filter(is.na(Y)==TRUE)
inTraining <- createDataPartition(train_$Y, p = .70, list = FALSE)
training <- train_[ inTraining,]
testing <- train_[-inTraining,]
# MODEL SVM
model_SVM <- train(
Y ~ ., training,
method = "svmPoly",
trControl = trainControl(
method = "cv", number = 10,
verboseIter = TRUE
)
)
summary(model_SVM)
SVMprediction <-predict(model_SVM, testing)
SVMprediction<-as.factor(SVMprediction)
testing$Y<-as.factor(testing$Y)
cmSVM <-confusionMatrix(SVMprediction,testing$Y) # ERROR 2
print(cmSVM)