I have a binary classification scenario with a dataset that is unbalanced (much more negatives than positives). When I train a classifier on this dataset I get a Precision-Recall AUC of 0.7.
Then I under-sampled the dataset to make it balanced. Then I trained the classifier on this balanced dataset and I got a PR-AUC of 0.9.
My question: is it correct to use PR-AUC and not ROC-AUC here? Because as I know PR-AUC is highly influenced by the class-imbalance, and now I'm afraid that I got a high PR-AUC with the balanced dataset because of the connection between PR-AUC and class-imbalance.
In other words, did under-sampling the dataset bias the result? Or was it really that under-sampling the dataset was a good thing to increase the classification performance?