I have an imbalanced dataset and I'm using
XGBoost to do binary classification. I used down sampling together with target and one hot encoding for train data. For test data I once used just the encodings and left it unbalanced and once tried with a balanced test dataset.
ROC AUC score was quite higher for the imbalanced test data than the balanced one. How is this possible? I thought for the
ROC AUC score there should not be any difference?