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Calibration Court – Fit & Recalibrate Labels
x402
Calibration Court – Fit & Recalibrate Labels is a paid API for AI agents from calibration-court.onrender.com, paid per call via x402, $0.05/call, status unknown (last checked 2026-10-29).
Fits an isotonic calibration model on user-supplied labelled {probability, outcome} pairs and applies it to recalibrate a set of new probabilities.
Price / call$0.05
Success—
StatusUnknown
How this service works
Paid (x402, USDC on Base). Fit isotonic calibration on YOUR labelled outcomes ({p,y} pairs) and recalibrate the given probabilities. Statele
Output
Returns recalibrated probability values for each input probability in the `probs` array, adjusted using an isotonic regression model fitted on the provided labelled {p, y} examples. The output reflects empirically corrected confidence scores aligned with actual observed outcomes.
Example use cases
Recalibrate overconfident classifier scores
“My fraud detection model is systematically overconfident — I have 500 labelled examples with the model's probability scores and the true fraud labels (0 or 1). Can you fit an isotonic calibration on those and recalibrate these 20 new scores: [0.95, 0.88, 0.76, 0.61, 0.55, 0.42, 0.3, 0.22, 0.18, 0.1, 0.08, 0.05, 0.97, 0.91, 0.83, 0.67, 0.49, 0.35, 0.15, 0.03]?”
Fix underconfident churn probability estimates
“Our churn model outputs probabilities that are too close to 0.5 — it's underconfident. I have 300 labelled {p, y} training examples. Fit isotonic calibration on those and then recalibrate these new churn probability estimates: [0.51, 0.53, 0.48, 0.55, 0.6, 0.45].”
Calibrate medical risk scores with clinical data
“I have clinical outcome data — 150 {p, y} pairs where p is a raw disease risk score and y is whether the patient actually had the event. Please fit isotonic calibration on my labelled examples and apply it to recalibrate these new patient risk scores: [0.12, 0.34, 0.67, 0.81, 0.05, 0.92].”
Technical details
Endpoint
POSThttps://calibration-court.onrender.com/api/v1/calibrate-labels?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have your own labelled {probability, outcome} ground-truth data and want to fit isotonic calibration from scratch on that data in a single stateless call. It is distinct from sibling endpoints that apply a pre-built calibration map — here you supply both the training examples and the probabilities to recalibrate in one request. Prefer it when you want a non-parametric, monotonic calibration without assuming a sigmoid or Platt shape.
Side effects
- Too few labelled examples provided — isotonic regression needs sufficient data points to fit a reliable mapping
- Probabilities outside [0,1] range cause validation errors
- y values that are not 0 or 1 integers are rejected by the schema
- Empty probs or examples arrays may return an error or identity mapping
- Payment failure (x402) if USDC balance is insufficient or transaction is rejected
- Service unavailable on cold start (Render free tier spin-up latency)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| probs | array | |
| examples | array |
Tags
x402
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