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.?
