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Issue Kind — Grouped Dataset Split
x402
Issue Kind — 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 2026-10-24).
Splits up to 200 labelled issue-classification text records (bug/request/question/other) into grouped train/test partitions and returns local dataset split deliverables with naive-Bayes baselines.
Price / call$0.01
Success—
StatusUnknown
How this service works
Issue kind - Grouped dataset split: Grouped dataset split for Issue kind. 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 bounded grouped dataset split deliverable containing partitioned train and test record sets drawn from the supplied labelled text records, along with local naive-Bayes baseline evaluation metrics for the four issue kind categories (bug, request, question, other).
Example use cases
Baseline evaluation for bug triage model
“I have 80 support tickets labelled as bug, request, question, or other — can you split them into grouped train and test sets and run a naive-Bayes baseline so I can see how well a simple model performs on issue classification?”
Dataset split for QA team review
“Our QA team needs a grouped train/test split of these 150 issue records (bugs, requests, questions, and other) so we can evaluate classification quality locally — can you generate the split and deliverable?”
Rapid prototype for issue classifier
“I'm prototyping an issue-kind classifier and have 50 labelled examples spread across bug, request, question, and other categories — split them into grouped partitions and give me a naive-Bayes baseline to sanity-check the data.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-issue-kind-dataset-split?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, local grouped train/test split and naive-Bayes baseline for a small labelled issue-classification dataset (up to 200 records) across the four issue kind categories (bug, request, question, other), without requiring hosted model infrastructure or foundation-model training. Ideal for prototyping, local evaluation, and sanity-checking labelled datasets before committing to larger ML pipelines.
Side effects
- Fewer than 2 records supplied — minimum item count not met
- More than 200 records supplied — maximum item count exceeded
- Record missing required fields (id, text, actual) — schema validation error
- Invalid 'actual' label value outside enum (bug/request/question/other) — rejected record
- Duplicate record IDs causing ambiguous grouping
- Insufficient class representation for meaningful split (e.g. only one class present)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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