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Calibration Court Recalibrate
x402
Calibration Court Recalibrate is a paid API for AI agents from calibration-court.onrender.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-29).
Maps a model's raw output probability to an empirically calibrated probability using resolved outcomes stored on the ledger
Price / call$0.01
Success—
StatusUnknown
How this service works
Paid (x402, USDC on Base). Map a model's raw probability to an empirically calibrated one using resolved outcomes on the ledger. Refuses wit
Output
A calibrated probability value (between 0 and 1) that has been adjusted based on empirically resolved outcomes stored on the ledger, replacing the raw model-output probability with one that better reflects real-world frequencies for that model and task type.
Example use cases
Fixing overconfident fraud classifier
“My fraud detection model outputs a confidence of 0.91 but I know it tends to be overconfident — can you recalibrate that raw probability for model 'xgboost-fraud-v2' on task type 'fraud_detection' so I get a more realistic estimate?”
Calibrating medical diagnosis probability
“I have a raw model probability of 0.67 from our diagnostic classifier called 'med-bert' on a binary_classification task — recalibrate it against resolved outcomes so I can trust the number before showing it to a clinician.”
Improving prediction market confidence score
“Before I submit this forecast, can you take the raw probability of 0.55 from model 'llama-forecaster' for task type 'event_prediction' and run it through calibration so the score reflects actual historical accuracy?”
Technical details
Endpoint
POSThttps://calibration-court.onrender.com/api/v1/recalibrate?utm_source=zero.xyzWhen to prefer
Use this endpoint when you have a raw probability from a specific ML model and want it adjusted to reflect empirical accuracy rather than the model's uncalibrated confidence. Ideal when deploying models in high-stakes contexts (medical, financial, forecasting) where calibration error is costly, and when resolved ground-truth outcomes for the model are available on the ledger. Prefer this over generic isotonic fitting endpoints when you do not have your own labelled outcome pairs and want to leverage shared resolved outcomes.
Side effects
- No resolved outcome data available for the specified model and task_type combination — endpoint may refuse or return an error
- Raw probability out of [0,1] range — validation error
- Unknown or unsupported model identifier — endpoint refuses the request
- Insufficient historical calibration data for reliable adjustment
- Payment failure via x402 — request rejected before processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| prob | number | |
| model | string | |
| task_type | string |
Tags
x402
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