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Return Reason Classifier – Supplied-Group Evaluation
x402
Return Reason Classifier – Supplied-Group Evaluation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).
Evaluates a supplied set of labeled return-reason text records (up to 200) broken down by group, computing per-group classification metrics against predicted and actual labels.
Price / call$0.01
Success—
StatusUnknown
How this service works
Return reason - Supplied-group evaluation: Supplied-group 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 supplied-group evaluation deliverable.
Output
A structured report containing per-group classification metrics for the return reason categories (damaged, wrong_item, unwanted, other), including accuracy, per-class precision/recall, and potentially a confusion summary — derived from the supplied labeled records using local naive-Bayes baselines and evaluation tooling. No hosted model training occurs.
Example use cases
Regional classifier audit for returns
“I have 180 return records with actual and predicted labels across three regional groups — north, south, and east. Each record has the return text and both the true label and my model's prediction. Run a group evaluation so I can see which region has the worst classification accuracy for return reasons like damaged or wrong_item.”
A/B test of two labeling strategies
“I ran two labeling strategies — call them group 'rule-based' and group 'ml-model' — on 100 return reason texts. I have the actual labels and each strategy's predicted label. Can you evaluate how each group performs across categories like unwanted and damaged?”
Product category return model benchmarking
“Before I deploy my return reason classifier to production, I want to benchmark it across product categories: electronics, apparel, and home. I have 150 labeled examples with actual and predicted return reasons (damaged, wrong_item, unwanted, other). Please run a group evaluation broken down by those categories.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-group-evaluation?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to evaluate a return-reason text classifier at a group/segment level using your own labeled dataset (up to 200 records), and you want per-group metrics rather than a single aggregate score. It is ideal for auditing classifier fairness across regions, product categories, or labeling strategies. It does not train or host a model — use it for evaluation and benchmarking only.
Side effects
- More than 200 records supplied — batch will be rejected or truncated
- Missing required fields (id, text, actual, predicted, group) on any record — validation error
- Invalid enum value for actual or predicted (must be one of: damaged, wrong_item, unwanted, other) — schema rejection
- Empty records array — returns no meaningful metrics
- Imbalanced groups with very few samples may yield unreliable metrics
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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