Micro AUC
In single-label problems the pooled positive rate is 1/K, providing a no-skill reference for micro average precision rather than a guaranteed floor.
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Macro AUC
Three classes with similar ROC-AUC score 0.96, 0.59 and 0.23 on average precision. Prevalence changes how these metrics should be read.
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Average Precision (AP)
Three different numbers go by the name PR-AUC. Which one average_precision_score returns, and why the difference is not cosmetic.
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AUC (Area Under the Curve)
The ranking view and the area view give the same value. Where the term is ambiguous, how ties are counted, and what AP is not.
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Precision–Recall AUC (PR-AUC)
The same model scores 0.79 or 0.04 depending only on how many negatives are in the test set. Why that is informative rather than a flaw.
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Imbalanced Data
Rare outcomes raise distinct questions about fitting, decision thresholds, and evaluation; resampling is one possible training intervention.
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Classification Metrics and Decision Thresholds
Ranking, probability, and decision are three separate things to evaluate.