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Machine Learning Metrics – Precision-Recall Curve
x402
Machine Learning Metrics – Precision-Recall Curve 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 precision-recall curve points, average precision (step-wise sum), and the baseline for binary classification labels and model scores.
Price / call$0.02
Success—
StatusUnknown
How this service works
Precision-recall curve points and average precision (step-wise sum) from binary labels and scores, with the baseline.
Output
Returns an array of curve points each with precision, recall, and threshold; a scalar average precision (step-wise sum); the no-skill baseline (fraction of positives); counts of positives and negatives; an optional summary verdict; and flags indicating whether the curve was truncated or the service is degraded.
Example use cases
Evaluating fraud detection model
“I have a fraud detection model — here are the true binary labels (1=fraud, 0=legit) and the raw model scores. Can you generate the full precision-recall curve and tell me the average precision so I can see how well it separates fraud from legitimate transactions? Labels: 0,0,1,0,1,1,0,1,0,0 Scores: 0.1,0.2,0.85,0.15,0.9,0.78,0.3,0.95,0.05,0.25”
Comparing model iterations on PR curve
“We just retrained our churn prediction model. Here are the ground truth labels and the new model's predicted scores — can you give me the precision-recall curve points and average precision so I can compare it against the old model's AP of 0.62? Labels: 1,0,1,1,0,0,1,0,1,0 Scores: 0.88,0.12,0.91,0.76,0.34,0.21,0.83,0.44,0.79,0.08”
Baseline-aware PR audit for medical classifier
“I need to audit a cancer screening classifier for a clinical report. Can you run the precision-recall curve and average precision on these binary diagnostic labels and confidence scores, and make sure I see the no-skill baseline too? Labels: 1,1,0,0,1,0,1,0,0,1 Scores: 0.95,0.87,0.2,0.33,0.78,0.41,0.82,0.19,0.27,0.91”
Technical details
Endpoint
GEThttps://ml-metrics.x402tookit.com/precision-recall?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need precision-recall curve data and average precision specifically for binary classification evaluation, especially when recall/precision tradeoff matters more than raw accuracy (e.g. imbalanced datasets, ranking models, medical screening). Prefer it over ROC-AUC when the positive class prevalence is low or when ranking quality at high precision is the priority. Use the sibling ROC endpoint if you need AUC-ROC instead.
Side effects
- Mismatched lengths between labels and scores arrays returns an error
- Non-binary values in labels (e.g. floats or multi-class) cause validation failure
- Empty or all-same-class labels may produce degenerate or undefined curve
- Exceeding 10000 values may be rejected or truncated
- Malformed comma-separated or JSON array string causes parse error
- Payment failure (x402) results in 402 response before computation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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