There are a lot of way to deal with class-imbalanced data like undersampling, oversampling, changing cost function etc.


Here is the post talking about them all.

I am working with an imbalanced multiclass classification problem and trying to solve it using XGBoost algorithm. I wanted to understand which method works best here. Since XGBoost already has a parameter called weights (which gives weight to each train record), would it be wise to directly use it instead of undersampling, oversampling, writing a cost function etc.?


1 Answer 1


I think using something like this could help in your case.

Hope this helps at least a bit!


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