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Document Type Confidence Calibration Bins
x402
Document Type 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 2026-10-28).
Computes confidence calibration bins for a document-type classifier (invoice, report, correspondence, other) from up to 200 labelled prediction records with associated probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Document type - Confidence calibration bins: Confidence calibration bins for Document type. 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 set of confidence calibration bins summarizing how well the classifier's predicted probability scores align with observed accuracy across binned probability ranges, including per-bin statistics useful for reliability diagrams and calibration error analysis.
Example use cases
Invoice classifier calibration audit
“I've been running a document classifier that flags invoices vs reports vs correspondence, and I want to know if its probability outputs are trustworthy — here are 150 labelled records with actual labels, predicted labels, and the four-class probability vectors. Can you compute the confidence calibration bins so I can see where the model is overconfident or underconfident?”
Pre-deployment reliability check
“Before I ship this document-type model to production, I need to verify that when it says 80% confident it's an invoice, it's actually right about 80% of the time. I have 200 test records with ids, texts, actual classes, predicted classes, and probabilities — please generate the calibration bins for me.”
Comparing two model versions
“I retrained my document classifier and want to check if calibration improved. I have the predicted probabilities and ground-truth labels for 120 records from the new model — can you give me the calibration bin breakdown so I can compare it against the old model's bins?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-document-type-calibration?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to evaluate the probability calibration of a document-type classifier operating over invoice, report, correspondence, and other categories, and you have up to 200 labelled prediction records with class probabilities. It is a lightweight, pay-per-call tool suited for spot evaluation and audit rather than large-scale training pipelines or deep learning retraining workflows.
Side effects
- Fewer than the minimum required records provided — returns error or degenerate bins
- Probability arrays that do not sum to 1 or contain out-of-range values — validation error
- Mismatch between number of classes and probability vector length (must be exactly 4) — schema error
- Records exceeding 200 — truncation or rejection
- Text fields exceeding 1000 characters — truncation or schema rejection
- Empty records array — error response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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