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Return Reason Classifier — Macro, Micro & Weighted Aggregate Scores
x402
Return Reason Classifier — Macro, Micro & Weighted Aggregate Scores 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 macro, micro, and weighted precision/recall/F1 aggregate scores for a return-reason text classification dataset with up to 200 labelled records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Return reason - Macro micro and weighted scores: Macro micro and weighted scores 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 macro micro and weighted scores deliverable.
Output
Returns macro, micro, and weighted aggregate classification metrics (precision, recall, F1) computed across the four return reason classes (damaged, wrong_item, unwanted, other) for the supplied labelled dataset of up to 200 records.
Example use cases
Benchmarking a new returns classifier
“I just trained a naive Bayes model to classify customer return reasons into damaged, wrong_item, unwanted, or other. I have 150 labelled test records with IDs, texts, actual labels, and my model's predictions — can you compute the macro, micro, and weighted F1 scores so I can see how well it's doing?”
Comparing two model versions
“I have predictions from two versions of my return reason model on the same 120 labelled records. Can you score version A for me first — give me macro, micro, and weighted precision, recall, and F1 — so I have a baseline to compare against?”
Auditing class imbalance impact
“Our return dataset is heavily skewed toward 'unwanted' returns. Can you run the macro, micro, and weighted aggregate scoring on my 80 labelled records so I can see how much the class imbalance is hurting my model's macro F1 versus its weighted F1?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-aggregate-scores?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, cheap ($0.01 USDC) aggregate classification metrics (macro, micro, weighted) for a bounded return-reason dataset without setting up a local ML evaluation environment. Ideal for lightweight model benchmarking or spot-checking classifier quality on up to 200 records in an agentic workflow where spinning up scikit-learn or a foundation model is impractical.
Side effects
- More than 200 records supplied — request rejected or truncated
- Records missing required fields (id, text, actual, predicted) — validation error
- Invalid enum values in actual or predicted fields — schema rejection
- Empty records array — no metrics can be computed
- Malformed JSON payload — HTTP 400 bad request
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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