The area under the receiver operating characteristic (ROC) curve is a scalar metric computed by numerical integration (trapezoidal rule) over the curve plotting true positive rate against false positive rate across all classification thresholds. It quantifies a model's overall ability to discriminate between positive and negative classes: a value of 1.0 means perfect discrimination, 0.5 means no better than random, and below 0.5 indicates systematic inversion. The metric persists as a stored numeric value derived from the ranked ordering of model predictions. [formal: auroc | substrate: mind | horizon: a moment | explicit: yes | epoch: 0.01]
Accepted ontology entry
area under roc curve
The area under the receiver operating characteristic (ROC) curve is a scalar metric computed by numerical integration (trapezoidal rule) over the curve plotting true positive rate against false positive rate across all classification thres…
Definition
Why it is in scope
A scalar metric that summarizes the entire ROC curve by computing the area under the curve, measuring a model's discrimination ability across all classification thresholds
Names and aliases
- area under roc curveen · CANONICAL
Relations from this entry
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AUC is computed by integrating the ROC curve. Remove the ROC curve and AUC has no curve to integrate — its operation collapses. The removal test passes.
- cmsetf6qd05wb3vv34zrnqmnkDEPENDS_ON →
AUC-ROC operates on probability forecasts: the ROC curve plots TPR vs FPR at every threshold of predicted probability. Remove probability forecasts and AUC-ROC has no input — it cannot compute or operate.
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Record identity
- Created
- Aug 5, 2026, 1:11 AM UTC
- Content hash
- ab2f6f2cf306c2f8940b0ebe01af8b2978cadeedfa2ba3922260befc91610d4c