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Meeting Topic Fixed-Holdout Learning Curve
x402
Meeting Topic Fixed-Holdout Learning Curve 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).
Generates a fixed-holdout learning curve for meeting topic classification (planning/progress/decision/review) using a naive-Bayes baseline on up to 200 labelled text records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Meeting topic - Fixed-holdout learning curve: Fixed-holdout learning curve 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 fixed-holdout learning curve deliverable.
Output
Returns a fixed-holdout learning curve showing how naive-Bayes classification accuracy (and related evaluation metrics) changes as the number of training records increases, computed against a held-out portion of the supplied labelled meeting topic records (planning, progress, decision, review).
Example use cases
Data sufficiency check for meeting classifier
“I have 150 labelled meeting notes tagged as planning, progress, decision, or review. Can you run a fixed-holdout learning curve on them so I can see whether 150 samples is enough for a naive-Bayes baseline to perform well?”
Benchmarking new meeting topic labels
“We just finished labelling 200 meeting excerpts into four categories — planning, progress, decision, and review. Run a learning curve against a fixed holdout so we can see how accuracy evolves as training data increases.”
Evaluating small-dataset classifier viability
“I only have 40 labelled meeting snippets split across planning and decision topics. Can you compute a fixed-holdout learning curve to tell me if a naive-Bayes model is even viable at this sample size?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-meeting-topic-learning-curve?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, cheap ($0.01 USDC) evaluation of how a naive-Bayes baseline learns to classify meeting topics (planning, progress, decision, review) from your own labelled text data, without needing a hosted model or foundation-model training. Ideal for data sufficiency analysis, sanity-checking label quality, or benchmarking small local datasets before committing to larger ML infrastructure.
Side effects
- Fewer than 2 records supplied — minimum input size not met
- Records missing required fields (id, text, actual) — validation error
- Text exceeds 1000 character limit per record — truncation or rejection
- Fewer than one example per class — model cannot learn distinctions
- More than 200 records supplied — exceeds maximum input size
- Invalid enum value for 'actual' field — only planning/progress/decision/review accepted
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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