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ROC Curve and AUC

TPR vs FPR across all thresholds; AUC summarizes discrimination ability.

A free, animated roc curve and auc you can read here or embed on any website, from Scrollchart.

ROC Curve and AUC

ROC Curve and AUCTPR vs FPR at every threshold. AUC = 1.0 is perfect; AUC = 0.5 is chance (the diagonal).0%20%40%60%80%100%False Positive Rate (1 - Specificity)0%20%40%60%80%100%True Positive Rate (Sensitivity)Chance (AUC 0.5)AUC benchmarks0.90 - 1.00Excellent0.80 - 0.90Good0.70 - 0.80Fair0.60 - 0.70Poor0.50 - 0.60FailKey insight:AUC is threshold-independent. Pick theoperating point to suitFN vs FP cost balance.AUC misleads at lowprevalence. Use AUPRCfor imbalanced classes.ROC AUC measures discrimination across all thresholds. No single threshold is optimal for all clinical contexts.

Three ROC curves overlaid (AUC 0.94 excellent, AUC 0.78 good, AUC 0.55 near-chance) on a TPR vs FPR plot. The AUC area under the excellent curve is filled. A diagonal chance line draws in first. An operating point at sensitivity 88%, specificity 82% is annotated on the best curve. A right panel lists AUC benchmark ranges. Bottom strip reinforces that AUC is threshold-independent.

Good for

  • Medical AI validation reports comparing multiple classifier models on the same diagnostic task using AUC as the primary discrimination metric
  • Clinical biomarker studies evaluating whether a new blood test outperforms an existing one at the population level before committing to a cutoff
  • Medical education explaining why a single sensitivity and specificity figure is an incomplete description of test performance across all thresholds

Source & accuracy

This roc curve and auc is an editorial illustration built to represent the concept accurately. Where it shows figures, they are typical or representative values chosen to make the relationship clear, not a single underlying dataset. The diagram and its explainer are reviewed and maintained centrally, and updated over time as understanding improves.

Reading a ROC curve

A receiver operating characteristic (ROC) curve plots the true positive rate (sensitivity) on the vertical axis against the false positive rate (one minus specificity) on the horizontal axis, tracing how the two trade off as the classification threshold sweeps across its full range. Each point on the curve corresponds to one threshold. A curve that hugs the top-left corner indicates a test that separates classes well, while a curve along the diagonal indicates performance no better than chance.

What AUC summarizes

The area under the ROC curve (AUC) condenses the whole curve into a single number between 0.5 and 1.0. It has an intuitive reading: the probability that the test ranks a randomly chosen positive case higher than a randomly chosen negative one. An AUC of 0.5 is chance and 1.0 is perfect discrimination. AUC measures discrimination, the ability to rank cases, and is independent of any single chosen threshold; it does not by itself describe calibration or the clinical consequences of false results.

This is a statistical explanation for educational use. Choosing an operating threshold for a real diagnostic test involves clinical judgment and is not implied by AUC alone.

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Reference

What this is
A free, embeddable, animated roc curve and auc for any website.
Who uses it
Medical educators, Researchers & academics, Data journalists.
How to embed
Copy one line of HTML. No signup. No watermark. Works in WordPress, Webflow, Ghost, Substack, plain HTML.
File size
iframe embed, ~80 KB gzipped (loads on demand, does not block your page paint).
License
Free forever. Editorial explainer text included; updated centrally over time.
URL
scrollchart.com/diagram/roc-curve-auc

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Frequently asked questions

Where can I get a free animated "ROC Curve and AUC" for my website?
Scrollchart provides "ROC Curve and AUC" as a free, embeddable animated diagram you can add to any website with one line of HTML. No signup is required and there is no watermark. The diagram and its explainer text are served from scrollchart.com, so the embed stays current without any maintenance on your end.
How do I embed a roc curve and auc diagram in a health or wellness blog?
Copy the HTML snippet from the Scrollchart diagram page and paste it into any post or page in your CMS. It works in WordPress, Webflow, Ghost, Substack, and plain HTML without any plugin or account. The diagram renders as animated SVG directly in your page, so search engines can index the accompanying explainer text.
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Likelihood Ratios

LR+ = sens/(1-spec); LR- = (1-sens)/spec. Multiplies pre-test odds to give post-test odds.