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Machine Learning Metrics: ROC Curve and AUC
x402
Machine Learning Metrics: ROC Curve and AUC is a paid API for AI agents from ml-metrics.x402tookit.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-11-02).
Computes ROC curve points and AUC from binary labels and model scores using the Mann-Whitney rank-sum identity, with tie-handling.
Price / call$0.02
Success—
StatusUnknown
How this service works
ROC curve points and AUC from binary labels and scores via the Mann-Whitney rank-sum identity, with ties counted half.
Output
Returns an array of ROC curve points each with false positive rate (FPR), true positive rate (TPR), and threshold; the AUC value via Mann-Whitney rank-sum; a trapezoid AUC; Gini coefficient; counts of positives and negatives; a summary string; a verdict; and optionally a degraded flag and disclaimer if the result is approximate or the curve was truncated.
Example use cases
Evaluating fraud detection model
“I just trained a fraud detection model and I want to know how well it ranks fraudulent vs legitimate transactions — here are the true labels: 0,0,1,0,1,1,0,1 and the predicted scores: 0.05,0.2,0.85,0.1,0.9,0.78,0.15,0.88. Can you give me the ROC curve points and AUC?”
Comparing two classifier outputs
“I have two sets of predictions for the same test set with labels 1,0,1,1,0,0,1,0,1,0 — can you compute the ROC AUC for scores 0.9,0.1,0.8,0.7,0.3,0.2,0.85,0.15,0.75,0.25 so I can benchmark against my baseline?”
Checking clinical risk score performance
“We built a clinical risk score to predict hospital readmission and I need to see the full ROC curve and AUC — the true readmission labels are 1,0,0,1,1,0,1,0 and the risk scores are 0.82,0.1,0.23,0.77,0.91,0.18,0.68,0.35.”
Technical details
Endpoint
GEThttps://ml-metrics.x402tookit.com/roc-auc?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need AUC and the full ROC curve for a binary classifier, especially when tie-handling via the Mann-Whitney rank-sum identity matters for correctness. Prefer it over precision-recall endpoints when the task is ranking/discrimination evaluation rather than threshold-specific performance. Use it when you need both the curve coordinates (for plotting) and the scalar AUC in one call.
Side effects
- Labels and scores arrays have different lengths — returns an error
- Non-binary labels (values other than 0/1) cause a validation failure
- Empty or malformed input arrays fail to parse
- All labels are the same class (all 0 or all 1) — AUC is undefined, may return null
- Very large inputs (over 10000 values) are rejected
- URL encoding issues with comma-separated values in query params
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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