I’m wondering how to deal (and if I have to) with values on train that are not present on test and vice versa.
Let’s say that category1 on my train set can have one of these possible values: A,B,C,D and E; On my test set, I can have: C,D,E,F and G
Clearly you can see that “A and B” occur on train but do not occur on test and “F and G” occur on test but do not occur on train.
I’m wondering if my model (any model) would benefit from updating A and B to something like “not in test” so the algorithm won’t bother to find “logic” on train categories that can’t be applied on test (or at least will bother less because there is only one cat now instead of many). Not sure if updating F and G to “not in train” makes any sense as well.
Two extra points: If mentioned only two categories missing for simplicity, There are actually more than two but the row count of missing values is very small, like 100 out of 150.000 both ways.