What does ROC-AUC really measure, and what's a good value?
easyAnswer
- AUC is the probability that a random positive example is scored higher than a random negative example (equivalent to the Mann-Whitney U statistic normalized). 0.5 = random; 1.0 = perfect ranking.
- Rules of thumb: 0.7 acceptable, 0.8 good, 0.9+ excellent.
- Beware: an AUC in isolation says nothing about calibration or the actual decision threshold — a classifier can have high AUC and still make horrible probability estimates.
Check yourself — multiple choice
- AUC measures accuracy
- P(positive score > negative score); ranking metric, threshold-independent
- AUC is the same as F1
- AUC only depends on the majority class
AUC = ranking probability. Threshold-independent; says nothing about calibration.
#metrics#classification
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