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Return Reason Prediction Error Records
x402
Return Reason Prediction Error Records is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Computes prediction error records for a naive-Bayes return-reason classifier on up to 200 supplied labelled text records
Price / call$0.01
Success—
StatusUnknown
How this service works
Return reason - Prediction error records: Prediction error records 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 prediction error records deliverable.
Output
A bounded set of prediction error records for each supplied labelled text item, identifying where the predicted return-reason class differed from the actual class, along with supporting probability information per record — up to 200 records processed in one call.
Example use cases
Diagnosing e-commerce return classifier mistakes
“I've got 150 labelled customer return requests where I know the true reason (damaged, wrong_item, unwanted, or other) and what my model predicted plus the four class probabilities for each — can you identify the prediction error records so I can see where the classifier is failing?”
Validating naive-Bayes model before deployment
“Before I deploy my return-reason classifier, I want to check its errors on a held-out set of 80 records with IDs, texts, actual labels, predicted labels, and probabilities — can you pull back the prediction error records for that batch?”
Auditing misclassifications for wrong-item returns
“My return reason model seems to be confusing 'wrong_item' and 'damaged' categories — I have 200 records with actual vs predicted labels and the probability vectors, can you compute the prediction error records so I can audit the specific misclassifications?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-errors?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a pre-labeled dataset for return-reason classification (with actual and predicted labels plus class probabilities) and need per-record error diagnostics from a lightweight naive-Bayes baseline evaluation — not a hosted model or foundation-model fine-tuning service. Ideal for small-scale offline evaluation, auditing classifier output, or rapid error analysis without spinning up your own ML infrastructure.
Side effects
- Records array exceeds 200 items — request rejected
- Missing required fields (id, text, actual, predicted, probabilities) on any record — validation error
- Probabilities array does not contain exactly 4 values between 0 and 1 — schema error
- Invalid enum value for actual or predicted (must be damaged, wrong_item, unwanted, or other) — rejection
- Payment of 0.01 USDC not completed via x402 — 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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