XGBoost evaluation metric unbalanced data - custom eval metric I have built a model using the xgboost package (in R), my data is unbalanced (5000 positives vs 95000 negatives), with a binary classification output (0,1).

I have performed cross validation with the evaluation metric AUC Area under the ROC curve which I now believe to be wrong since this is better used for balanced data sets.

I analyse the final results of the model using the Area Under the Precision Recal curve (AUPRC) and the Matthews Correlation Coefficient (MCC), however I now believe that I should have been evaluating the cross validation models with the AUPRC and MCC also and completely forget about the AUROC.

I cannot find much in the literature which uses CV with the evaluation metric of AUPRC and MCC.

I just want to make sure that I am thinking correctly and that my previous evaluation method is wrong and the AUPRC / MCC would be a better way to go.
I feel you are mixing few things:

1:20 is not exactly a class imbalanced data. Classification algorithms work well with this ration also. If you still feel so, you can oversample your positive class or undersample negative class.
AUROC generally to compare different models. Like one from logistic and other from Xgboost.( It also ensures robustness of models), Generally AUROC is not used for model accuracy/ getting cut-off values.
Logloss/ misclassification rate/ F1 score/ MCC any of these can be used based on your requirement of classification. If you are predicted cancer , then you might not want to miss any patient and might be Ok to false positive, then focus should be on recall. f1 gives balance of precision and recall. Log loss is used (Log Loss heavily penaltises classifiers that are confident about an incorrect classification) when you don't want to have false positive. These links might help.

https://machinelearningmastery.com/classification-accuracy-is-not-enough-more-performance-measures-you-can-use/
https://www.r-bloggers.com/making-sense-of-logarithmic-loss/
https://towardsdatascience.com/evaluation-metrics-for-classification-409568938a7d