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Task Type Per-Label Precision and Recall
x402
Task Type 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 2026-10-15).
Computes per-label precision and recall metrics for a task-type classifier (research, writing, analysis, implementation) given up to 200 labelled text records with actual and predicted labels.
Price / call$0.01
Success—
StatusUnknown
How this service works
Task type - Per-label precision and recall: Per-label precision and recall for Task type. 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 task type (research, writing, analysis, implementation), computed from the supplied actual versus predicted label pairs across up to 200 records. Each label receives its own precision and recall score, enabling granular diagnosis of classifier performance by category.
Example use cases
Diagnosing classifier confusion between labels
“I have 120 labelled records where my model predicts whether each document is research, writing, analysis, or implementation. Can you compute per-label precision and recall so I can see which task types my classifier is getting confused on?”
Benchmarking a new task-type model version
“I just retrained my task-type classifier and want to compare how it performs on each label. Here are 180 test records with the actual and predicted labels — give me precision and recall broken down by research, writing, analysis, and implementation.”
Validating task-type labels before production deploy
“Before I push this classifier to production, can you run a per-label precision and recall check on these 75 labelled task-type records to make sure none of the four categories — research, writing, analysis, implementation — are significantly underperforming?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-task-type-per-label?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need per-label (per-class) precision and recall metrics for a task-type classifier operating over the four fixed categories: research, writing, analysis, implementation. It is ideal for lightweight, local evaluation of small to medium labelled datasets (up to 200 records) without needing a hosted model or foundation-model infrastructure. Prefer it over generic ML evaluation APIs when your labels are specifically these four task types and you need a cheap ($0.01 USDC), bounded, deterministic computation.
Side effects
- More than 200 records supplied — input is capped at 200 and excess may be rejected or truncated
- Missing required fields (id, text, actual, predicted) in any record causes validation failure
- Invalid label values outside the allowed enum (research, writing, analysis, implementation) cause schema rejection
- Empty records array returns no meaningful metrics
- Highly imbalanced label distribution may yield undefined precision or recall for rare labels
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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