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Calibration Court — Isotonic Probability Calibration
x402
Calibration Court — Isotonic Probability Calibration is a paid API for AI agents from mcp-x402.onrender.com, paid per call via x402, $0.05/call, status unknown (last checked 2026-10-29).
Fits isotonic regression calibration on user-supplied labelled outcome pairs and recalibrates a set of raw probabilities using that fitted model.
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 scores adjusted using an isotonic regression model fitted on the provided labelled outcome pairs. The output corrects systematic over- or under-confidence in raw probabilities so they better reflect empirical frequencies.
Example use cases
Classifier confidence score correction
“My fraud detection model outputs raw probabilities but they're poorly calibrated — I have 200 labelled examples with actual fraud outcomes. Can you fit isotonic calibration on those pairs and give me back corrected probabilities for my new batch of scores?”
Medical risk model calibration
“I'm working with a clinical risk model that gives uncalibrated probabilities. I have ground-truth patient outcome labels paired with the model's predictions — please fit isotonic regression calibration on those and recalibrate these new probability estimates.”
Ad click-through prediction tuning
“Our ad CTR model's raw scores don't reflect real click rates. I have labelled {probability, actual_click} pairs from last month — can you fit isotonic calibration on them and recalibrate today's predicted click probabilities?”
Technical details
Endpoint
POSThttps://mcp-x402.onrender.com/gate/calibration-court/api/v1/calibrate-labels?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have your own labelled ground-truth outcome pairs and need stateless, per-call isotonic probability calibration without maintaining server-side model state. Prefer it over Platt scaling when your calibration data may not follow a sigmoid shape, or when you need a non-parametric monotone correction. Ideal for agents that already have labelled validation sets and want a lightweight pay-per-call calibration step rather than integrating a full ML framework.
Side effects
- Malformed or empty probs/examples arrays return a 400 error
- Non-monotone violations handled internally by isotonic regression pooling
- Insufficient labelled examples may produce poorly fitted calibration
- Payment not received or invalid USDC/x402 transaction returns 402 Payment Required
- Server cold-start on Render may introduce latency on first call
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| probs | array | |
| examples | array |
Tags
x402
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