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Training Question Per-Label Precision and Recall
x402
Training Question 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 2026-10-28).
Computes per-label precision and recall metrics for a training question classifier using up to 200 supplied labelled text records with actual and predicted labels.
Price / call$0.01
Success—
StatusUnknown
How this service works
Training question - Per-label precision and recall: Per-label precision and recall for Training question. 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 metrics for each of the four label categories (concept, procedure, example, assessment), computed from the supplied records containing actual and predicted label pairs. Returned as a bounded deliverable covering up to 200 records.
Example use cases
Evaluating educational content classifier
“I've got 120 labelled training question records — each tagged as either concept, procedure, example, or assessment — along with my model's predictions. Can you calculate precision and recall for each label so I can see where my classifier is weakest?”
Identifying underperforming label categories
“My text classifier seems to confuse 'procedure' and 'example' labels a lot. I have 80 records with actual and predicted labels — can you give me a per-label precision and recall breakdown so I can pinpoint which categories are dragging down performance?”
Benchmarking a naive Bayes baseline model
“I'm running a baseline naive Bayes model on 200 training question records across four labels: concept, procedure, example, and assessment. Can you compute the per-label precision and recall so I can document the baseline metrics before trying more advanced models?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-training-question-per-label?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, per-label precision and recall evaluation for a 4-class training question classifier (concept, procedure, example, assessment) on small datasets up to 200 records, without needing to spin up a hosted model or use a foundation model. Ideal for quick local evaluations, baseline comparisons, or auditing label-level classifier performance cheaply at $0.01 per call.
Side effects
- Exceeding the 200-record limit may result in rejection or truncation
- Missing required fields (id, text, actual, predicted) in any record will cause a validation error
- Invalid label values outside the allowed enum (concept, procedure, example, assessment) will be rejected
- Empty records array will return no meaningful metrics
- Payment failure or insufficient USDC balance will block the request
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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