A classic question with an unclear answer, is it better to have an overfitted model performing better on a Cross-Validation setting, or a non-overfitted model performing worse?
In this context, higher overfitting means higher discrepancy between train and test sets.
Overfitted: Avg Test AUROC 0.82 & Avg Train AUROC 0.96
Non-overfitted: Avg Test AUROC 0.78 & Avg Train AUROC 0.81
Which model should you use?