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Feedback Theme Per-Label Precision and Recall
x402
Feedback Theme Per-Label Precision and Recall is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Computes per-label precision and recall metrics for a feedback theme classifier across up to 200 labelled text records with actual and predicted labels.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feedback theme - Per-label precision and recall: Per-label precision and recall for Feedback theme. 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 scores for each feedback theme category (quality, price, delivery, support), computed from the supplied actual vs. predicted label pairs across up to 200 records.
Example use cases
E-commerce feedback classifier audit
“I've labelled 120 customer reviews with actual themes like quality, price, delivery, and support, and I have my model's predicted labels too — can you run per-label precision and recall so I see exactly which theme my model is most confused about?”
Naive Bayes baseline evaluation
“I trained a simple naive Bayes model to classify feedback into quality, price, delivery, and support, and I want to evaluate it — here are 80 records with actual and predicted labels, please give me per-label precision and recall breakdowns.”
Support team classifier QA
“Our support team's auto-tagger assigns one of four feedback themes to each ticket, and I need to validate how well it's doing — I have 200 records with the correct label and the predicted label for each, can you compute precision and recall per label?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-feedback-theme-per-label?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, bounded per-label precision and recall evaluation for a feedback theme classifier without requiring a hosted model or large-scale ML infrastructure. Ideal for small dataset spot-checks (up to 200 records), local evaluation pipelines, or naive Bayes baseline comparisons across the four feedback categories (quality, price, delivery, support). Not suitable for full model training, multi-class datasets beyond these four labels, or large-scale batch evaluation.
Side effects
- More than 200 records submitted — input will be rejected or truncated
- Missing required fields (id, text, actual, predicted) in any record — validation error
- Invalid label values outside the enum (quality, price, delivery, support) — schema rejection
- Empty records array — no metrics computable
- All records belonging to one label only — some per-label metrics undefined or zero
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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