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Return Reason Confidence Calibration Bins
x402
Return Reason Confidence Calibration Bins is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).
Computes confidence calibration bins for a return-reason classifier, given up to 200 labelled records with predicted probabilities and actual labels.
Price / call$0.01
Success—
StatusUnknown
How this service works
Return reason - Confidence calibration bins: Confidence calibration bins for Return reason. Up to 200 supplied labelled text records. Local dataset tools, evaluation and small naive-Bayes baselines; no hosted model or foundation-model training. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded confidence calibration bins deliverable.
Output
A confidence calibration bins deliverable: the supplied records are grouped into probability bins and the endpoint returns bin-level statistics showing how predicted confidence scores align with observed accuracy, enabling reliability diagram construction and calibration diagnosis for the four return-reason classes (damaged, wrong_item, unwanted, other).
Example use cases
Evaluating e-commerce return classifier calibration
“I've got 120 customer return records where I know the real reason (damaged, wrong_item, unwanted, or other), plus what my classifier predicted and the 4 probability scores it output — can you compute the confidence calibration bins so I can see if the model is overconfident or underconfident?”
Validating naive-Bayes model before production deploy
“Before I ship my return-reason model, I want to check its calibration. I have 200 labelled examples with actual and predicted return reasons and the probability arrays — please give me the calibration bins so I can decide if it needs isotonic regression or Platt scaling.”
Diagnosing miscalibration in a damaged-item detector
“My return classifier seems to be too confident when it predicts 'damaged' — can you run calibration bin analysis on my 80 test records with their actual labels, predicted labels, and probability vectors to show me where the probability estimates are off?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-calibration?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need lightweight, pay-per-call confidence calibration bin computation for a return-reason classifier without standing up any infrastructure. It is purpose-built for the four return-reason classes (damaged, wrong_item, unwanted, other) and accepts up to 200 records with predicted probabilities, making it ideal for spot evaluation during model development or A/B testing. Prefer it over general-purpose ML platforms when you need a fast, cheap, bounded calibration check with no model hosting or training overhead.
Side effects
- Fewer than the minimum required records supplied — endpoint may return an error or degenerate bin output
- Probability arrays not summing to 1 or containing values outside [0,1] — validation error
- More than 200 records supplied — truncated or rejected input
- Mismatched enum values in actual/predicted fields — schema validation failure
- All records belonging to a single class — bins for minority classes may be empty or undefined
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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