Question about class weights in xgboost

I'm training an xgboost model in R using the caret package. I added class weights to the train control because my binary outcome is very skewed. However, the model with weights has an AUROC of 0.49, whereas the model without the weights has an AUROC of 0.88. I have other models too including logistic regression, random forest, etc, and their AUROC increased after implementing the weights. So why does it decrease it so much for XGBoost, is it because XGB …

bigger binary caret control datascience logistic regression package random regression training xgboost