# Calibration Court – Fit & Recalibrate Labels

> 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.

## Facts

- Endpoint: POST https://calibration-court.onrender.com/api/v1/calibrate-labels?utm_source=zero.xyz
- Price: $0.05/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-29
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/calibration-court-fit-recalibrate-labels-041ff1e9
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_HdXUmG13GLaTixKMClz0Z

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability calibration-court-fit-recalibrate-labels-041ff1e9 -d '<json body>'
```

Example prompt: Calibrate these raw model probabilities using my labelled historical examples — here are 200 {p, y} pairs where p is the model's predicted probability and y is the actual outcome (0 or 1), and I need you to fit an isotonic calibration model on them and then recalibrate this list of new probabilities: [0.9, 0.75, 0.4, 0.2].

## When to prefer this

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.

## Known failure modes

- 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)

## 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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "probs": {
   "type": "array",
   "items": {
    "type": "number"
   }
  },
  "examples": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "p",
     "y"
    ],
    "properties": {
     "p": {
      "type": "number"
     },
     "y": {
      "enum": [
       0,
       1
      ],
      "type": "integer"
     }
    }
   }
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/calibration-court-fit-recalibrate-labels-041ff1e9/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from calibration-court.onrender.com](https://www.zero.xyz/host/calibration-court.onrender.com/llms.txt)