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Meeting Topic Classifier — Confidence Calibration Bins
x402
Meeting Topic Classifier — 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 2026-10-15).
Computes confidence calibration bins for a meeting topic classifier (planning/progress/decision/review) given up to 200 labelled records with predicted probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Meeting topic - Confidence calibration bins: Confidence calibration bins for Meeting topic. 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 for the meeting topic classifier: binned breakdowns showing mean predicted confidence versus actual accuracy per bin, enabling detection of overconfidence or underconfidence across the planning, progress, decision, and review classes.
Example use cases
Diagnosing overconfidence in meeting classifier
“My meeting topic model keeps predicting 'decision' with 90%+ confidence but is often wrong — can you run confidence calibration bins on these 80 labelled records (actual vs predicted labels and probabilities) so I can see which confidence ranges are miscalibrated?”
Pre-deployment calibration check for NLP pipeline
“Before I ship my meeting transcript classifier to production, I want to verify its probability outputs are well-calibrated. Here are 150 records with actual meeting types, predicted types, and class probabilities — please compute the calibration bins.”
Comparing model versions with calibration metrics
“I retrained my meeting topic model and want to see if the new version is better calibrated than the old one. I have 120 records with actual labels, predictions, and probabilities from the new model — give me the confidence calibration bins so I can compare them to my baseline.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-meeting-topic-calibration?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to evaluate the probability calibration quality of a meeting topic classifier (planning/progress/decision/review) using your own labelled dataset. Ideal for post-training evaluation, pre-deployment checks, or comparing model versions. Prefer this over generic calibration tools when working specifically in the meeting transcript/topic classification domain with four defined classes.
Side effects
- Fewer than the minimum required records provided — returns error or empty bins
- Probabilities array does not sum to 1 or contains values outside [0,1] — validation error
- More than 200 records submitted — request rejected or truncated
- Mismatched actual/predicted enum values outside allowed set — schema validation failure
- Payment not included or insufficient — 402 payment required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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