A model achieves AUC = 0.50 on the test set. What does this indicate?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because AUC = 0.50 means the model's ranking of positive vs. negative examples is equivalent to random chance.
Full explanation below image
Full Explanation
B is correct because AUC = 0.50 means the model's ranking of positive vs. negative examples is equivalent to random chance. A (perfect separation) would be AUC = 1.0. C is wrong; accuracy and AUC are different metrics. D (calibration) addresses reliability of probability values, not discrimination ability.