ROC-AUC Calculator (from TPR/FPR points)
Trapezoidal area under the ROC curve from your (FPR, TPR) operating points — the threshold-independent ranking score.
AUC is the probability that the model scores a random positive higher than a random negative — a pure ranking quality measure, independent of any threshold. 0.5 is a coin flip; an AUC below 0.5 usually means your labels or scores are flipped.
Formula
About ROC-AUC Calculator (from TPR/FPR points)
ROC-AUC is the most-reported single number for binary classifiers because it's threshold-independent: it summarizes performance across every possible cutoff into one value with a beautiful interpretation — the probability that the model ranks a random positive example above a random negative one. This calculator computes AUC by trapezoidal integration of the (FPR, TPR) operating points you provide, plus the equivalent Gini coefficient. 0.5 is random guessing, 1.0 is a perfect ranker; values below 0.5 almost always signal flipped labels rather than a uniquely bad model.
How to use ROC-AUC Calculator (from TPR/FPR points)
- 1Enter your values into ROC-AUC Calculator (from TPR/FPR points) — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use ROC-AUC Calculator (from TPR/FPR points)?
- ✓Computes ROC-AUC instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: AUC = Σ trapezoid areas under the ROC curve.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
What does an AUC of 0.85 actually mean?+
That if you pick one random positive and one random negative example, the model gives the positive a higher score 85% of the time. It's a ranking quality measure — it says nothing about a specific threshold's accuracy, only about how well the model orders examples by likelihood of being positive.
ROC-AUC vs PR-AUC — when to use which?+
ROC-AUC can look deceptively high on heavily imbalanced data because the huge true-negative pool dominates the false-positive rate. PR-AUC (precision-recall) focuses on the positive class and is far more informative when positives are rare (fraud, disease, anomalies). Report PR-AUC for imbalanced problems.
Why might my AUC be below 0.5?+
Almost always a sign your predictions or labels are inverted — a model worse than random in a consistent direction is just a good model with flipped sign. Swap the predicted-probability orientation (1 − p) or fix the label encoding, and the AUC becomes 1 − (current value).
Does AUC depend on the classification threshold?+
No — that's its defining strength. It integrates over all thresholds, so it characterizes the model's intrinsic ranking ability independent of where you eventually set the cutoff. To evaluate a specific deployed threshold, use precision, recall, F1 or balanced accuracy at that point instead.
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