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Return Reason - Grouped Dataset Split
x402
Return Reason - 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 2026-10-12).
Splits up to 200 labelled return-reason text records into a grouped train/test dataset partition for local naive-Bayes baseline evaluation.
Price / call$0.01
Success—
StatusUnknown
How this service works
Return reason - Grouped dataset split: Grouped dataset split for Return reason. 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 containing the supplied labelled text records (up to 200) partitioned by return reason category (damaged, wrong_item, unwanted, other), suitable for local naive-Bayes baseline training and evaluation. No hosted model or foundation-model training is performed.
Example use cases
E-commerce return classifier baseline
“I have 80 labelled customer return comments tagged as damaged, wrong_item, unwanted, or other — can you split them into a grouped dataset partition so I can benchmark a naive-Bayes classifier on them?”
QA team dataset evaluation prep
“Our QA team collected 150 return-reason text samples with actual labels. Split them into grouped train/test sets so we can evaluate our local classification model's performance.”
Small-scale ML experiment setup
“I'm running a quick ML experiment on return reasons and have 40 short text records labelled damaged or unwanted. Give me a grouped dataset split I can use to test a simple baseline model.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-dataset-split?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a small labelled dataset of return-reason text (up to 200 records) and need a quick, cheap grouped train/test split for local naive-Bayes baseline evaluation. Ideal for e-commerce or logistics teams running lightweight ML experiments without requiring a hosted or foundation model. Not suitable for large-scale datasets, deep learning pipelines, or scenarios requiring a trained inference model as output.
Side effects
- Fewer than 2 records supplied — minimum item count not met
- More than 200 records supplied — exceeds maxItems limit
- Invalid or missing 'actual' enum value (must be one of: damaged, wrong_item, unwanted, other)
- Record text exceeds 1000 characters — truncation or rejection
- Missing required fields (id, text, actual) on any record — validation error
- Payment failure via x402 protocol — endpoint returns 402 with payment details
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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