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Training Question Confidence Calibration Bins
x402
Training Question 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 text classifier that categorizes training questions into concept, procedure, example, or assessment classes.
Price / call$0.01
Success—
StatusUnknown
How this service works
Training question - Confidence calibration bins: Confidence calibration bins for Training question. 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 confidence calibration bins showing how the classifier's predicted probabilities map to observed accuracy — typically bucketed probability ranges with the mean predicted confidence and actual accuracy per bin, enabling identification of over- or under-confidence across the concept, procedure, example, and assessment label classes.
Example use cases
Diagnosing overconfident classifier on course content
“My text classifier for categorizing training questions seems overconfident — it says 90% probability but is wrong a lot. Can you run confidence calibration bins on these 80 labeled records (each with actual and predicted labels of concept, procedure, example, or assessment, plus a 4-element probability array) so I can see how badly miscalibrated it is?”
Validating naive Bayes model before deployment
“Before I ship this naive Bayes classifier for tagging training questions, I want to check its calibration. I have 120 records with actual labels, predicted labels, and probability vectors across concept, procedure, example, and assessment — can you generate the calibration bin breakdown so I can decide if I need to apply Platt scaling?”
Comparing calibration across two classifier versions
“I'm comparing two versions of my training question classifier and want calibration bin stats for version B. Here are 60 records with the actual class, predicted class, and 4-value probability distribution for each — please give me the confidence calibration bins so I can compare reliability to version A.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-training-question-calibration?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need lightweight, local confidence calibration analysis on up to 200 labeled records for a 4-class training question classifier (concept, procedure, example, assessment) without hosting a model or calling a foundation model API. Prefer it over general ML evaluation platforms when you want a quick, cheap ($0.01 USDC) calibration bin report on small datasets, especially in agentic pipelines that use x402 micropayment flows.
Side effects
- Fewer than the minimum required records provided — may return empty or degenerate bins
- Probability vectors that don't sum to 1 or contain values outside [0,1] — validation error
- Records with missing required fields (id, text, actual, predicted, probabilities) — schema rejection
- More than 200 records supplied — truncation or rejection based on limit
- Mismatch between actual/predicted enum values and allowed classes — error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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