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I'm trying to build a classifier using Xgboost on some high dimensional data, the problem I'm having is that I have the prior knowledge that the output probabilities should be ascending regarding a feature(say x), but I don't know how can I make the model understand this!
For example for a data point with features Feat I want to have:
predict_proba(Feat[x=1]) <= predict_proba(Feat[X=2])
where the rest of the features are the same.
You can enforce such monotonicity with the monotone_constraints (hyper)parameter:
https://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html
https://stackoverflow.com/questions/43076451/how-to-enforce-monotonic-constraints-in-xgboost-with-scikitlearn
