I am using XGBoost for payment fraud detection. The objective is binary classification, and the data is very unbalanced. One out of every 3-4k transactions is fraud.
I would expect the best way to evaluate the results is a Precision-Recall (PR) curve, not a ROC curve, since the data is so unbalanced.
However in the eval_metric options I see only area under the ROC curve (AUC), and there is no PR option. https://github.com/dmlc/xgboost/blob/master/doc/parameter.md
Also the documentation recommends AUC http://xgboost.readthedocs.io/en/latest/how_to/param_tuning.html
Does it make sense to not use a Precision-Recall (PR) curve?