What approach for creating a multi-classification model based on all categorical features (1 with 5,000 levels)? I have a data set I'm trying to create a predictor model for.  The 5 features and outcome are all categorical data.  One of the features contains 5,000 unique levels. While the other 4 are all under 100 levels.  The output variable in my dataset contains 400 unique levels.

How do I handle this scenario for modeling?

My approach was going to be:


Convert the 5,000 level feature to a sequential number list via a lookup table.
Convert the remaining 4 features using One Hot Encoding.
Convert the output variable to a sequential number list via a lookup table.
Use xgboost algorithm.


I don't think I can bin the 5,000 level feature because they are user id's.  So to me, there is no way to bin them.
Personally, I think that building a predictor on such user Ids is not that efficient neither useful. Can you change your problem in a different manner? 

When we do any analysis we drop those ids as they are of no use they are only the incidents in a large population and we try to group them in categories which describe their features. For example, users which have good eyesight will play basketball even if it rains.

So try to think like that.