I have a silly question - I have in model several variables with 100+ possible labels each (like Country that stores country code, Industry storing industry code, etc.) - I have to somehow convert this to a data consumed by a classifier.
I could encode these variables with OHE and have over 400 new variables, but then I have another issue - some of labels in these variables need to stay (so some of newly created variables will stay whatever their statistical importance is) and I cannot drop them, therefore I won't be able to reduce the number of variables from 400 to 50, but rather to 380 and I have a pretty small data set.
How would You guys approach it? Does it even make sense to encode it? I had a thought on that to replace these labels with some unified dict labels like country -> country risk with labels low, medium, high, but that one is not simple as well, but for "political" reasons this will not go through.