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Meeting Topic Label and Prediction Audit
x402
Meeting Topic Label and Prediction Audit 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).
Audits classification labels and predictions for meeting topic text records across four categories (planning, progress, decision, review), returning evaluation metrics and diagnostics.
Price / call$0.01
Success—
StatusUnknown
How this service works
Meeting topic - Label and prediction audit: Label and prediction audit 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 label and prediction audit deliverable.
Output
Returns a structured audit report covering classification accuracy, per-class metrics (precision, recall, F1), confusion matrix statistics, probability calibration diagnostics, and comparisons against naive-Bayes baselines for the four meeting topic categories: planning, progress, decision, and review.
Example use cases
Classifier QA before production deployment
“I've got 120 labeled meeting notes where each one is tagged as planning, progress, decision, or review, and I ran them through my classifier. Can you audit the predictions against the actual labels and tell me the accuracy, any problematic categories, and where probabilities look off?”
Detecting mislabeled training data
“Before I retrain my meeting topic model, can you audit these 80 labeled records — I suspect some of the 'review' entries were actually mislabeled as 'decision'. Give me a breakdown of where predictions diverged from actuals so I can spot annotation errors.”
Benchmarking a naive-Bayes meeting classifier
“I built a simple naive-Bayes classifier for meeting topics and have 200 text samples with their predicted labels, actual labels, and class probabilities across planning, progress, decision, and review. Can you run a prediction audit so I can see how it benchmarks and where it fails?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-meeting-topic-label-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a small labeled dataset (up to 200 records) of meeting-topic text and want a local, lightweight audit of classifier quality without sending data to a hosted model or foundation model. Ideal for NLP pipeline QA, training-data validation, or benchmarking a naive-Bayes baseline on the specific planning/progress/decision/review taxonomy. Not suitable for large-scale batch evaluation or training new models.
Side effects
- More than 200 records submitted — payload exceeds limit
- Missing required fields (id, text, actual, predicted, probabilities) on any record — validation error
- Probabilities array does not contain exactly 4 values summing near 1.0 — schema rejection
- Invalid enum value for actual or predicted outside allowed set — rejection
- Empty records array — no audit can be performed
- Payment not included or insufficient — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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