One-vs-Rest (OvR) AUROC
Each OvR AUROC compares one class with its own negative mixture. Interpret the mixture and finite-sample variation before comparing class scores.
-
-
Gini Coefficient
The same credit scores gave a credit-risk Gini of 0.6706 and an economics Gini of 0.4601. The analogy between the two curves is not an identity.
-
Discriminatory Power
AUROC and Gini preferred one model, KS and average precision preferred the other. All four are described as measures of discriminatory power.
-
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.
-
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.
-
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.
-
ROC-AUC
Two models with the same area can differ 2.5-fold in what they catch at a workable false positive rate. What to report beside the number.