"Error: Missing data in columns" due to fix.factors.prediction=TRUE

[feinmann]

library(mlr)

train_data <- data.frame(
  A = runif(100), B = factor(sample(c("A", "B"), 100, replace = T)))

test_data <- data.frame(
  A = runif(100), B = factor(sample(c("A", "B", "C"), 100, replace = T)))

lrn <- makeLearner("regr.ranger", fix.factors.prediction = TRUE)

train_task <- makeRegrTask(
  data = train_data,
  target = "A"
)

model <- train(lrn, train_task)

predictions <- predict(model, newdata = test_data)

Gives Error: Missing data in columns: B., although there is no missing data. Same for classification.

Kind regards

[pat-s]

This issue sounds familiar to me - I think it hit me some time in the past as well.
I cannot tell you when I will have time to look into this.

Thanks a lot for the reprex in the first place!

[jakob-r]

The reason is that the new factor C is converted to an NA because of fix.factors.prediction = TRUE.
As you can see in the documentation this is feature was intended for cases where the test data has less factors than the training set. However, it has the side effect that it reduces the levels to the one seen in the training and R then just sets new factor levels to NA. Maybe some learners can deal better with an NA then with an unseen factor? However this is not really intended and definitely has to go into the documentation of the fix.factors.prediction argument.

Also we might want to deal with it better.
#2771 is kind of related.

[pat-s]

The PR from Jakob provides a good approach to the problem. Most likely there is not much else we can do in such situations to account for all possible issues with missing data in prediction scenarios.