Macro AUC#
AUC averaged equally across per-class scores.
Important
✨ AI-generated content. This page was written with the assistance of an AI language model and is provided as a learning aid. Despite careful review, it may still contain mistakes, omissions, or out-of-date information. Whether you are new to the topic, a team lead, or a senior practitioner, treat it as a starting point rather than an authoritative reference: read it critically and independently verify anything you act on (code, commands, figures, and factual claims) against official documentation and primary sources before relying on it.
What it is#
Macro AUC averages the per-class AUCs (from One-vs-Rest) with equal weight — every class counts the same, no matter how rare or common. It answers how well does the model do on the average class?
Macro vs micro#
The contrast is micro AUC, which pools every class’s true/false-positive contributions into one global curve, effectively weighting by prevalence so frequent classes dominate. Macro treats a class with 10 samples exactly like one with 10,000.
When to use which#
Macro is the choice when the rare classes matter as much as the common ones (you want minority performance to show), while micro (or a weighted macro) better reflects overall accuracy on imbalanced data. Reporting both reveals whether a good score is carried by the majority classes.
Theme: Classification & Averaging Metrics · All terminology
Hint
Mind map — connected ideas
Micro AUC · One-vs-Rest (OvR) · Multiclass Classification · ROC-AUC (Receiver Operating Characteristic – Area Under Curve, = AUROC) · Multiclass AUROC · Partial AUC (pAUC)
Hint
More in Classification & Averaging Metrics
Accuracy · AUC (Area Under the Curve) · Average Precision (AP) · Binary Classification · Classification Probability · Discriminatory Power · F1-score · Gini Coefficient · Harmonic Mean · Log Loss (also called Logarithmic Loss or Cross-Entropy Loss) · Macro AUROC (Macro-Averaged AUROC) · Macro Averaging · Macro F1 · Macro Precision
See also
Source article Adapted (context, re-expressed) in our own words from: Macro AUC (insightful-data-lab.com).