randomForest does not work when training set has m

Question

When trying to test my trained model on new test data that has fewer factor levels than my training data, predict() returns the following:

Type of predictors in new data do not match that of the training data.

My training data has a variable with 7 factor levels and my test data has that same variable with 6 factor levels (all 6 ARE in the training data).

When I add an observation containing the "missing" 7th factor, the model runs, so I'm not sure why this happens or even the logic behind it.

I could see if the test set had more/different factor levels, then randomForest would choke, but why in the case where training set has "more" data?

Answer 1

R expects both the training and the test data to have the exact same levels (even if one of the sets has no observations for a given level or levels). In your case, since the test dataset is missing a level that the train has, you can do

test$val <- factor(test$val, levels=levels(train$val))

to make sure it has all the same levels and they are coded the same say.

(reposted here to close out the question)