In XGBoost would we evaluate results with a Precision Recall curve vs ROC? 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?
I am answering my own question, almost two years later.  XGBoost now has a new eval metric aucpr.

https://xgboost.readthedocs.io/en/latest/parameter.html#learning-task-parameters