What does AUC (area under the curve) refer to?

What does AUC (area under the curve) refer to?

• A. A measure of the total area under a calibration curve.
• B. The ratio of false positive rate to true positive rate.
• C. Area under the ROC curve representing a model's ability to discriminate between classes.
• D. The area under the precision-recall curve.

Answer: (C)

The main idea is that AUC measures how well a model can separate the two classes across all possible thresholds. It does this by summarizing the ROC curve, which plots true positive rate against false positive rate as you vary the threshold for classifying positives. A higher AUC means the model tends to give higher scores to positive cases than to negative ones, indicating strong discriminatory power. In fact, AUC can be interpreted as the probability that a randomly chosen positive instance will be ranked above a randomly chosen negative one. The value ranges from 0.5 (no better than random) to 1.0 (perfect separation). This is distinct from calibration, which is about how close predicted probabilities are to observed frequencies, and from the area under the precision-recall curve, which assesses different aspects of performance, especially with imbalanced data.

The main idea is that AUC measures how well a model can separate the two classes across all possible thresholds. It does this by summarizing the ROC curve, which plots true positive rate against false positive rate as you vary the threshold for classifying positives. A higher AUC means the model tends to give higher scores to positive cases than to negative ones, indicating strong discriminatory power. In fact, AUC can be interpreted as the probability that a randomly chosen positive instance will be ranked above a randomly chosen negative one. The value ranges from 0.5 (no better than random) to 1.0 (perfect separation). This is distinct from calibration, which is about how close predicted probabilities are to observed frequencies, and from the area under the precision-recall curve, which assesses different aspects of performance, especially with imbalanced data.