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Return Reason Classifier – Grouped Three-Fold Cross-Validation
x402
Return Reason Classifier – 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 cross-validation baseline evaluation on up to 200 labelled return-reason text records using a naive-Bayes classifier, returning accuracy and fold metrics.
Price / call$0.01
Success—
StatusUnknown
How this service works
Return reason - Grouped three-fold baseline evaluation: Grouped three-fold baseline evaluation 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 three-fold baseline evaluation deliverable.
Output
Returns a grouped three-fold cross-validation result for the supplied labelled return-reason dataset, including per-fold and aggregate accuracy metrics produced by a local naive-Bayes baseline classifier. No hosted model is involved; the computation is self-contained and deterministic for the supplied records.
Example use cases
Validate return reason labelling quality
“I've got 120 labelled customer return comments — each tagged as damaged, wrong_item, unwanted, or other — can you run a three-fold cross-validation on them to see if the labels are consistent enough for a naive-Bayes baseline to learn from?”
Benchmark before full model training
“Before I invest in training a bigger model, can you run a grouped three-fold baseline evaluation on my 200 return reason records (damaged, wrong_item, unwanted, other) so I have a naive-Bayes accuracy score to compare against?”
Audit annotation consistency for a new return category
“We just added an 'unwanted' return category to our labelling scheme — here are 80 newly labelled records across all four categories, can you do a grouped three-fold cross-validation to check whether the naive-Bayes baseline can distinguish unwanted from the others?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-cross-validation?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a cheap, fast, local baseline cross-validation for a return-reason text classification task with up to 200 labelled examples and four fixed categories (damaged, wrong_item, unwanted, other). It is ideal for sanity-checking label quality or annotation consistency before committing to larger model training. Prefer alternatives if you need foundation-model training, hosted inference, more than 200 records, or categories beyond the four supported labels.
Side effects
- Fewer than 2 records supplied — minimum input not met
- More than 200 records supplied — exceeds maximum batch size
- Invalid or missing 'actual' label not in enum (damaged, wrong_item, unwanted, other)
- Records with empty or missing 'text' field cause validation errors
- Insufficient class diversity for meaningful three-fold grouping
- Payment not completed — x402 micropayment of 0.01 USDC required per call
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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