ROC-AUC (Receiver Operating Characteristic – Area Under Curve, = AUROC)#
The probability a random positive outranks a random negative.
Important
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What it is#
ROC-AUC is the area under the ROC curve, which plots the true positive rate (recall) against the false positive rate as the decision threshold sweeps from 0 to 1. It condenses that whole curve into one number in \([0, 1]\).
How to read it#
1.0 is a perfect classifier, 0.5 is random guessing. It has a clean probabilistic meaning — the chance that a randomly chosen positive is scored higher than a randomly chosen negative — so it measures ranking quality, independent of any single threshold and invariant to the score scale.
The caveat#
Because the false positive rate has all the true negatives in its denominator, ROC-AUC can look optimistically high on imbalanced data where negatives dominate — a model can score well while still flooding a rare positive class with false alarms. There, PR-AUC is more honest.
Theme: Classification & Averaging Metrics · All terminology
Hint
Mind map — connected ideas
Precision–Recall AUC (PR-AUC) · Precision (a.k.a. Positive Predictive Value, PPV) · Multiclass AUROC · Binary Classification · Classification Probability · Multiclass Classification
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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 AUC · Macro AUROC (Macro-Averaged AUROC) · Macro Averaging · Macro F1
See also
Source article Adapted (context, re-expressed) in our own words from: ROC-AUC (Receiver Operating Characteristic – Area Under Curve, = AUROC) (insightful-data-lab.com).