Looking for the circumstances of when we should use a ROC curve vs. a Precision Recall curve.
Example of answers I am looking for:
Use a ROC Curve when:
- you have a balanced or imbalanced dataset (Source).
- when the cost of false positives and false negatives is roughly equal (needs verification)
Use a Precision Recall Curve when:
- you have a imbalanced dataset with way more positives than negatives (Source).
- when the cost of false positives is higher than false negatives (needs verification)