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Meeting Topic Grouped Three-Fold Cross-Validation
x402
Meeting Topic Grouped Three-Fold Cross-Validation 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).
Runs a grouped three-fold baseline cross-validation on up to 200 labelled meeting-topic text records using a naive-Bayes classifier to estimate classification accuracy.
Price / call$0.01
Success—
StatusUnknown
How this service works
Meeting topic - Grouped three-fold baseline evaluation: Grouped three-fold baseline evaluation 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 grouped three-fold baseline evaluation deliverable.
Output
A cross-validation report containing the grouped three-fold evaluation results for the supplied labelled meeting-topic records, including per-fold accuracy, overall accuracy, and baseline naive-Bayes performance metrics across the four categories (planning, progress, decision, review).
Example use cases
Pre-training label quality check
“I've got 150 labelled meeting notes — each one marked as planning, progress, decision, or review — and I want to know if the labels are clean enough to train on. Can you run a grouped three-fold baseline evaluation and tell me the cross-validation accuracy?”
Comparing topic separability across teams
“Here are 120 meeting excerpts from our engineering team labelled as planning, progress, decision, or review. Run the grouped three-fold naive-Bayes baseline on them so I can see how distinguishable these topics are before I invest in a heavier model.”
Benchmarking a new annotation scheme
“We just re-annotated 80 meeting summaries with four categories — planning, progress, decision, and review — using a new rubric. Can you run the grouped three-fold cross-validation baseline to see if the new scheme scores better than our old 61% accuracy?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-meeting-topic-cross-validation?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a small labelled dataset of meeting-topic text (up to 200 records) and want a quick, cheap baseline accuracy estimate using local naive-Bayes cross-validation before committing to fine-tuning a larger model. It is ideal for label quality audits, annotation sanity checks, and establishing a performance floor. Prefer alternatives if you need a hosted model, foundation-model fine-tuning, inference on unlabelled data, or datasets larger than 200 records.
Side effects
- Fewer than 2 records supplied — returns validation error
- Records missing required fields (id, text, actual) — returns schema error
- More than 200 records supplied — request rejected with maxItems error
- Invalid category value in 'actual' field (not one of planning/progress/decision/review) — enum validation failure
- Insufficient class representation for folding — may return degenerate or unreliable fold results
- Payment not settled via x402 protocol — returns 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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