Explore the options below.

Which of the following ROC curves produce AUC values greater than 0.5?

This is the best possible ROC curve, as it ranks all positives above all negatives. It has an AUC of 1.0.

In practice, if you have a "perfect" classifier with an AUC of 1.0, you should be suspicious, as it likely indicates a bug in your model. For example, you may have overfit to your training data, or the label data may be replicated in one of your features.

This is the worst possible ROC curve; it ranks all negatives above all positives, and has
an AUC of 0.0. If you were to reverse every prediction (flip negatives to positives and
postives to negatives), you'd actually have a perfect classifier!

This ROC curve has an AUC of 0.5, meaning it ranks a random positive example
higher than a random negative example 50% of the time. As such, the
corresponding classification model is basically worthless, as its predictive
ability is no better than random guessing.

This ROC curve has an AUC between 0.5 and 1.0, meaning it ranks a random positive
example higher than a random negative example more than 50% of the time. Real-world
binary classification AUC values generally fall into this range.

This ROC curve has an AUC between 0 and 0.5, meaning it ranks a random positive
example higher than a random negative example less than 50% of the time.
The corresponding model actually performs worse than random guessing! If you
see an ROC curve like this, it likely indicates there's a bug in your data.