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Confidence Calibration Report
x402
Confidence Calibration Report is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.047/call, status unknown (last checked 2026-10-16).
Computes binary Brier score and count-weighted Expected Calibration Error (ECE) for up to 256 score/outcome pairs using equal-width probability bins
Price / call$0.047
Success—
StatusUnknown
How this service works
Compute binary Brier score and count-weighted ECE for up to 256 declared held-out score/outcome pairs. Supports probability or percent scores, normalized to probabilities. Equal-width bins use exact rational boundaries: lower inclusive, upper exclusive, final bin includes 1. Empty bins report null means/gaps. No clinical validity claim.
Output
Returns a structured calibration report including the Brier score, count-weighted ECE, per-bin statistics (mean predicted score, mean outcome, count, gap), exact rational bin boundaries, an input digest, and a receipt digest for auditability. Empty bins report null for mean and gap values.
Example use cases
Forecasting model calibration check
“I have 200 held-out prediction pairs from my binary forecasting model — each with a probability score and a 0/1 outcome. Can you run a calibration report with 10 bins to see my Brier score and ECE? The scores are in probability format and data is synthetic.”
Classifier confidence audit
“My classifier outputs confidence scores between 0 and 1 for a binary event. I've got 80 held-out public test cases and want to know how well-calibrated those scores are — please use 8 bins and compute the Brier score and ECE for me.”
Percent-scale prediction evaluation
“I have a set of 120 forecasts expressed as percentages (0–100) with their actual binary outcomes. Can you calibrate them using 5 equal-width bins and tell me the Brier score and expected calibration error? Mark the data as synthetic.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/micro/confidence-calibration-report?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, auditable, deterministic computation of Brier score and ECE over a small-to-medium held-out binary prediction dataset (up to 256 pairs). It is ideal for model evaluation pipelines, forecasting audits, or calibration checks where exact rational bin boundaries, reproducibility via input/receipt digests, and no external data dependencies matter. Prefer it over manual computation when you want structured bin-level diagnostics and auditability built in.
Side effects
- Fewer than 1 or more than 256 pairs provided — request rejected
- binCount outside 2–20 range — validation error
- scoreUnits not matching actual score range (e.g. percent scores submitted as probability) — distorted results
- evaluationSplit value other than declared-held-out-offline — schema error
- Payment not included or insufficient — x402 payment required response
- Malformed pair entries (missing score or outcome) — parsing error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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