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Project Status Per-Label Precision and Recall
x402
Project Status 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 project-status text classifier using up to 200 supplied labelled records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Project status - Per-label precision and recall: Per-label precision and recall for Project status. 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 bounded per-label precision and recall report covering each of the four project status classes (planned, active, blocked, complete), derived from up to 200 supplied labelled text records with actual and predicted labels.
Example use cases
Diagnosing blocked-class recall drops
“My project status classifier seems to miss a lot of 'blocked' items — I have 120 records with actual and predicted labels. Can you compute per-label precision and recall so I can confirm whether blocked recall is the problem?”
Validating a new classifier before deployment
“Before we ship our project status model, I want to verify it performs well on all four categories. Here are 200 labelled samples with actual and predicted values — give me the per-label precision and recall for planned, active, blocked, and complete.”
Comparing model iterations on project status
“I retrained my project status text classifier and have 80 labelled test records. Can you calculate per-label precision and recall across planned, active, blocked, and complete so I can compare this version to our baseline?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-project-status-per-label?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, low-cost per-label precision and recall evaluation for a project-status text classifier without a hosted model or foundation-model training requirement. Ideal for lightweight diagnostic passes on small labelled datasets (≤200 records) where you want label-level insight into classifier errors across planned, active, blocked, and complete categories.
Side effects
- Fewer than the minimum required labelled records provided — returns error or degenerate metrics
- Records missing required fields (id, text, actual, predicted) — validation error
- Labels outside the allowed enum (planned, active, blocked, complete) — rejected input
- Exceeding 200 records limit — request truncated or rejected
- Empty records array — no metrics computable
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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