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Knowledge Topic Grouped Dataset Split
x402
Knowledge Topic Grouped Dataset Split 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).
Splits a labelled knowledge-topic text dataset into grouped train/test partitions, preserving class balance across setup, usage, troubleshooting, and reference categories.
Price / call$0.01
Success—
StatusUnknown
How this service works
Knowledge topic - Grouped dataset split: Grouped dataset split for Knowledge 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 dataset split deliverable.
Output
A grouped dataset split deliverable that partitions the supplied labelled text records into train and test sets with groups aligned to the four knowledge topic classes (setup, usage, troubleshooting, reference), suitable for local evaluation and naive Bayes baseline testing.
Example use cases
Documentation classifier dataset prep
“I have 150 labelled knowledge articles tagged as setup, usage, troubleshooting, or reference — can you split them into grouped train and test sets so I can benchmark a topic classifier?”
Support knowledge base evaluation
“Take these 80 annotated support docs with setup and troubleshooting labels and create a grouped dataset split for me so I can measure how well a naive Bayes model generalises across each topic group.”
Knowledge topic baseline model validation
“I want to validate a small naive Bayes knowledge topic model — please do a grouped split on my 200 labelled records across setup, usage, troubleshooting, and reference so I can run a proper evaluation.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-knowledge-topic-dataset-split?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 pre-labelled knowledge topic text records (setup, usage, troubleshooting, reference) and need a grouped, class-aware train/test split for local evaluation or naive Bayes baseline benchmarking — without access to a hosted model or foundation model training pipeline. Ideal for lightweight, cost-effective dataset preparation at $0.01 per call.
Side effects
- Fewer than 2 records supplied — minimum item count not met
- More than 200 records supplied — exceeds maximum item count
- Record missing required fields (id, text, actual) — schema validation error
- Invalid label value outside allowed enum (setup/usage/troubleshooting/reference)
- Text field exceeds 1000 character limit per record
- Insufficient records per class to form a meaningful grouped split
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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