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Return Reason Classifier — Per-Label Precision & Recall
x402
Return Reason Classifier — Per-Label Precision & Recall 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 per-label precision and recall metrics for a return-reason text classifier across up to 200 labelled records with actual and predicted labels.
Price / call$0.01
Success—
StatusUnknown
How this service works
Return reason - Per-label precision and recall: Per-label precision and recall 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 per-label precision and recall deliverable.
Output
A per-label breakdown of precision and recall for each return reason category (damaged, wrong_item, unwanted, other), computed from the supplied actual-vs-predicted label pairs across up to 200 records.
Example use cases
Diagnose weak label in return classifier
“My return-reason classifier seems to mix up 'unwanted' and 'wrong_item' returns. I've got 120 labelled examples with both the actual and predicted labels — can you compute precision and recall for each label so I can see exactly where it's going wrong?”
Benchmark naive Bayes return model
“I just trained a simple naive Bayes model on return reason data and I want to know how it performs per category. Here are 80 records with the text, the actual return reason (damaged, wrong_item, unwanted, or other), and the model's predictions — give me the precision and recall for each label.”
Pre-launch model quality check
“Before we go live with our automated returns triage, I need to verify the classifier's per-label precision and recall on a held-out set. I have 200 records with actual and predicted return reason labels — can you run the evaluation and tell me which labels are underperforming?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-per-label?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, cheap ($0.01 USDC) per-class precision and recall evaluation for a return-reason text classifier without setting up local tooling. It is ideal for spot-checking model quality on small labelled datasets (up to 200 records) across the four standard return reason categories. Prefer alternatives if you need macro/micro aggregated F1, confusion matrices, or support for custom label sets beyond the four built-in categories.
Side effects
- More than 200 records submitted — endpoint enforces an upper limit
- Missing required fields (id, text, actual, predicted) on any record — validation error
- Invalid label values outside the enum (damaged, wrong_item, unwanted, other) — schema rejection
- Empty records array — no metrics can be computed
- Payment not completed via x402 — request blocked
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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