I’ve read that precision-recall (PR) curves are preferred over AUC-ROC curves when a dataset is imbalanced as there’s more of a focus on the model’s performance in correctly identifying the minority/positive class.
At what point (rule of thumb?) does it make more sense to primarily use PR to evaluate a classifier instead of AUC-ROC score? I imagine if the dataset has 40% positive class, AUC is still appropriate? But what about at 30% or 20% positive class? What level is considered “imbalanced” where PR is preferred?