ROC-AUC Can Mislead on Rare Events; Precision-Recall Metrics Reveal False Alarms

A tutorial explains that a classifier scoring a high ROC-AUC can still flood reviewers with irrelevant alerts when the target event is rare. Using a hypothetical wildfire social-media monitor, it notes that positives make up only about 1% of posts in the example. ROC-AUC measures how well positives are ranked above negatives, while precision and recall focus on the usefulness of flagged posts. The article argues that for imbalanced problems such as fraud, security, maintenance, and crisis monitoring, precision-recall evaluation can give a more meaningful picture of performance. It promises a reproducible Python demonstration comparing both metrics on the same classifier.