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Return Reason Label Distribution
x402
Return Reason Label Distribution 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).
Computes label distribution statistics across up to 200 labelled return-reason text records, categorized as damaged, wrong_item, unwanted, or other.
Price / call$0.01
Success—
StatusUnknown
How this service works
Return reason - Label distribution: Label distribution 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 label distribution deliverable.
Output
A bounded label distribution deliverable showing the count and/or proportion of records assigned to each return reason class (damaged, wrong_item, unwanted, other) across the supplied dataset of up to 200 labelled records.
Example use cases
Pre-training dataset audit
“Before I train my return reason classifier, can you check whether my 150 labelled records are balanced across the damaged, wrong_item, unwanted, and other categories? Here are the records with their IDs, text, and actual labels.”
Class imbalance detection for ML pipeline
“I'm building a returns triage model and I think my training data might be skewed heavily toward 'damaged'. Can you compute the label distribution across all four return reason classes for my 80 labelled examples so I can see if I need to oversample?”
QA check on labelling batch
“My team just finished labelling a batch of 120 customer return comments as damaged, wrong_item, unwanted, or other. Can you show me the breakdown of how many fall into each category so I can QA the labelling effort?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-label-balance?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, cheap ($0.01 USDC) statistical summary of label balance in a small labelled dataset (up to 200 records) for the specific four-class return reason taxonomy (damaged, wrong_item, unwanted, other). It is ideal for pre-training dataset audits, class imbalance detection, and QA checks on labelling batches without needing a hosted model or full ML pipeline.
Side effects
- Fewer than 2 records supplied — minimum item count not met
- More than 200 records supplied — exceeds maximum item count
- Record missing required 'id', 'text', or 'actual' field — validation error
- Invalid 'actual' enum value not in [damaged, wrong_item, unwanted, other] — schema rejection
- Empty or overly long text field exceeding 1000 characters — field-level validation failure
- Payment not processed — x402 payment required before response is returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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