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Return Reason Label and Prediction Audit
x402
Return Reason Label and Prediction Audit 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).
Audits up to 200 labelled text records for return-reason classification accuracy, computing evaluation metrics against actual vs. predicted labels across four categories.
Price / call$0.01
Success—
StatusUnknown
How this service works
Return reason - Label and prediction audit: Label and prediction audit 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 and prediction audit deliverable.
Output
A bounded audit report covering the supplied labelled records, including accuracy metrics, per-class performance breakdown, comparison of actual vs. predicted return reason labels (damaged, wrong_item, unwanted, other), and identification of misclassified records based on the supplied class probabilities.
Example use cases
Post-deployment model accuracy check
“I just deployed a naive-Bayes return reason classifier and I have 150 labelled records with actual labels, predicted labels, and probabilities for damaged, wrong_item, unwanted, and other — can you run a full prediction audit and show me the accuracy breakdown?”
Label quality audit before retraining
“Before I retrain my return reason model, I want to audit 200 of my labelled records to see how consistent the actual labels are compared to what the model predicted — here are the texts, actual reasons, predicted reasons, and class probabilities.”
Spot-checking misclassified returns
“My return reason model keeps confusing damaged with wrong_item — can you audit these 80 records with their actual and predicted labels plus probabilities so I can see exactly where it's going wrong?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-label-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, low-cost ($0.01) audit of a return-reason text classifier on up to 200 pre-labelled records. Ideal for spot-checking model quality, validating label consistency, or evaluating naive-Bayes baselines without access to a hosted ML platform. Best suited for e-commerce or logistics teams tracking return categorization accuracy across the four standard classes: damaged, wrong_item, unwanted, and other.
Side effects
- More than 200 records supplied — batch is capped at 200
- Missing or malformed probability arrays (must have exactly 4 values summing to ~1.0)
- Invalid actual or predicted label values outside the four allowed enum values
- Records with text exceeding 1000 characters or IDs exceeding 80 characters
- Empty records array returns no meaningful audit output
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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