I am considering trying to model a very large number of categories. I read that catboost has an algorithm to convert categories to ordered floats to optimize decision tree splits. Does the algorithm have a maximum number of categories that it supports? Also any advice on using it with large number of categories would be appreciated.
Does catboost have a maximum number of categories?
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Catboost documentation does not mention a specific upper limit, so I think you are only bounded by your RAM if the number of categories is really large.
There is a mention of 255 categories, but this refers to the default value chosen in a specific scenario rather than the maximum value.