A model achieves AUC = 0.50 on the test set. What does this indicate?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — if your model has AUC = 0.50, it's doing no better than random guessing when ranking positives against negatives.
Full Explanation
AUC = 0.50 means the model's ranking of positives versus negatives is equivalent to random chance. Perfect separation would be AUC = 1.0. Accuracy and AUC are different metrics, so 50% accuracy is not implied. Calibration affects probability reliability, not discrimination.