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Error in model.frame.default - building a decision tree model

I'm trying to create a decision tree on a dataset that I have split into training and test data sets.
My complete data set contains 21 variables, all of which are numeric.
The complete error message was this:
Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = attr(object, : factor member_id has new levels 100000105, 100000134, 100000205, 100000229, 100000253, 100000278, 100000280, 100000325, 100000331, 100000358, 100000375, 100000387, 100000411, 100000454, 100000494, 100000509, 100000516, 100000582, 100000606, 100000615, 100000625, 100000631, 100000636, 100000647, 100000661, 100000663, 100000684, 100000708, 100000722, 100000730, 100000811, 100000813, 100000845, 100000851, 100000889, 100000893, 100000996, 100001030, 100001038, 100001067, 100001069, 100001107, 100001127, 100001140, 100001144, 100001218, 100001248, 10000130, 100001315, 100001340, 100001430, 100001431, 100001443, 100001449, 100001454, 100001501, 100001547, 100001609, 100001682, 100001686, 100001693, 100001764, 100001771, 100001819, 100001820, 100001871, 100001873, 10000191, 10000195, 100001965, 100001985, 100002, 100002003, 100002015, 100002043, 100002071, 100002098, 100002103, 100002125, 100002152, 10000223, 100002263, 100002280, 100002333, 100002408, 100002464, 100002628, 10000264
The variable member_id is an ID variable.
I got the error after entering this code:
workshopdata[is.na(workshopdata)]=0
split=sample.split(workshopdata$Transacted, SplitRatio=0.60)
train=subset(workshopdata, split==TRUE)
testd=subset(workshopdata, split==FALSE)
fit = rpart(Transacted~., data=train, method="class")
predict=predict(fit, newdata=testd, type='class')
For context, Transacted is my dependent variable, train is the training dataset and testd is my test dataset.
How do I debug this error? If you need any more context, I can always share that.
submitted by kappa23 to RStudio

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