📏 AUC (Area Under the Curve)

AUC (Area Under the Curve)#

The area under a ROC or PR curve summarising performance across thresholds.

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 Despite careful review, it may still contain mistakes, omissions, or out-of-date information.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#

AUC — the area under the curve — condenses an entire ROC curve into one number by measuring the area beneath it. It ranges from 0 to 1: 1 is perfect, 0.5 is random guessing, and below 0.5 means the scores are inverted.

Its meaning#

AUC has a clean interpretation — it is the probability that a random positive is scored higher than a random negative (the Wilcoxon–Mann–Whitney statistic). So it measures how well the model ranks positives above negatives, independent of any single threshold.

Why it beats accuracy#

AUC is threshold-invariant and always calibrated so 0.5 = useless, unlike accuracy, which is misleading under imbalance (90% accuracy is trivial when 90% of data is negative). But on heavily imbalanced data the PR-AUC often tells a more honest story, since ROC-AUC can look optimistic.

Theme: Classification & Averaging Metrics · All terminology

Hint

Mind map — connected ideas

ROC Curve (Receiver Operating Characteristic) · ROC-AUC (Receiver Operating Characteristic – Area Under Curve, = AUROC) · Precision–Recall AUC (PR-AUC) · Model Score · Partial AUC (pAUC) · Accuracy

Hint

More in Classification & Averaging Metrics

Accuracy · 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 AUC · Macro AUROC (Macro-Averaged AUROC) · Macro Averaging · Macro F1 · Macro Precision