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Delx Calibration ECE
x402
Delx Calibration ECE is a paid API for AI agents from api.delx.ai, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-14).
Computes Expected Calibration Error (ECE) from forecast probabilities and binary outcomes to measure probabilistic model calibration quality.
Price / call$0.001
Success—
StatusUnknown
How this service works
Expected calibration error from forecast/outcome pairs. Call when scoring probability model calibration offline. Returns ece and n as deterministic JSON for $0.001 USDC via x402 on Base. First-party local math only—no RPC, live market feed, API keys, storage, or mediagen. Advisory only; the caller owns capital, risk, and production controls.
Output
Returns a JSON object containing the ECE score (a float indicating mean calibration error across bins), the total number of samples evaluated (n), and a schema identifier string (e.g. 'delx/util-calibration-ece/v1') for versioning and provenance tracking.
Example use cases
Evaluate classifier confidence calibration
“I have a binary classifier and I want to check how calibrated it is — my predicted probabilities are [0.2, 0.5, 0.7, 0.9, 0.3] and the actual outcomes were [0, 1, 1, 1, 0]. Can you compute the ECE using 5 bins?”
Audit weather forecast reliability
“I have a set of rain probability forecasts [0.1, 0.3, 0.6, 0.8, 0.95, 0.4] and whether it actually rained [0, 0, 1, 1, 1, 0]. Measure how well-calibrated those forecasts are using 10 bins.”
Compare model calibration before and after Platt scaling
“After applying Platt scaling my recalibrated probabilities are [0.15, 0.45, 0.65, 0.85, 0.5] and the true labels are [0, 0, 1, 1, 1]. Can you compute the ECE with 5 bins so I can see if calibration improved?”
Technical details
Endpoint
POSThttps://api.delx.ai/api/v1/x402/calibration-eceWhen to prefer
Use this endpoint when you need a fast, pay-per-call ECE computation without standing up your own calibration library, especially within agent workflows that already use the Delx utility ecosystem. Prefer it over implementing ECE manually when you need a stateless, versioned, auditable calibration measurement that integrates cleanly into automated pipelines.
Side effects
- Mismatched array lengths between forecasts and outcomes returns a validation error
- Forecast probabilities outside [0,1] range cause schema rejection
- Zero bins or negative bin count results in a bad request error
- Empty forecast/outcome arrays may return ECE of 0 or an error
- Non-binary values in outcomes array (anything other than 0 or 1) may cause incorrect results or errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | Number of calibration bins |
| outcomes | array | Binary outcomes 0/1 aligned with forecasts |
| forecasts | array | Forecast probabilities in [0,1] |
Output
Schema · Response
{
"type": "json",
"example": {
"n": 4,
"ece": 0.225,
"schema": "delx/util-calibration-ece/v1"
}
}Tags
x402
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