I have a dataset that contains 99.95% 0's and 0.05% 1's as the target. The dataset contains million rows. I want to build a binary classification model that predicts almost all the 1's correctly while keeping the false positives at minimum.
I have read it somewhere that AUC-PRC is a better metric for the above scenario compared to AUC-ROC. Is it correct?