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Release Change Confidence Calibration Bins
x402
Release Change 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 2026-10-11).
Computes confidence calibration bins for a release-change classifier (feature/fix/documentation/maintenance) from up to 200 labelled text records with predicted probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Release change - Confidence calibration bins: Confidence calibration bins for Release change. 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
Returns a bounded confidence calibration bins deliverable: binned statistics showing how well the classifier's predicted probabilities align with actual outcomes across the feature, fix, documentation, and maintenance classes, enabling assessment of model reliability and potential miscalibration.
Example use cases
Auditing overconfident release classifier
“My release-change model keeps predicting 'feature' with 90% confidence but is wrong a lot — can you compute calibration bins for these 120 records with their actual labels, predicted labels, and probability scores so I can see where it's miscalibrated?”
Pre-deployment model quality check
“Before shipping this release-change classifier to production, I want to verify its confidence scores are trustworthy. Here are 200 labelled examples with actual categories, predictions, and 4-class probabilities — please calculate the calibration bins for me.”
Comparing two classifier versions
“I've got two sets of release-change predictions from different model versions and I want to see which one has better-calibrated probabilities. Can you run calibration bins on this first batch of 80 records — actual labels, predicted labels, and probability arrays all included?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-release-change-calibration?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need calibration bin statistics specifically for a 4-class release-change classifier (feature/fix/documentation/maintenance) on small datasets up to 200 records, without needing a hosted model or foundation-model training. It is ideal for lightweight local evaluation workflows and post-hoc probability calibration audits on existing classifier outputs.
Side effects
- More than 200 records submitted — input truncated or rejected
- Probability arrays not summing to 1 or containing values outside [0,1]
- Probability arrays with fewer or more than 4 elements — schema violation
- Invalid or missing class labels (must be one of: feature, fix, documentation, maintenance)
- Empty records array — no calibration computable
- Record IDs exceeding 80 characters or missing — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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