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The ability to define different custom loss functions for different subsets of the training data would be my use case. I can do this with lightgbm (a bit hack-ish but gets the job done). My loss function is not constant as it is part of an optimization framework. Obviously gradients and Hessians should be available but there are frameworks for approximating those.
Ability to define arbitrary loss function just like in
lightgbm
would be great. Any such plans?The text was updated successfully, but these errors were encountered: