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Release Change Classifier — Per-Label Precision and Recall
x402
Release Change Classifier — 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 naive-Bayes release-change classifier across up to 200 labelled text records with four categories: feature, fix, documentation, maintenance.
Price / call$0.01
Success—
StatusUnknown
How this service works
Release change - Per-label precision and recall: Per-label precision and recall for Release change. 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
Returns precision and recall values for each of the four labels (feature, fix, documentation, maintenance), computed from the supplied records' actual vs. predicted label pairs — giving a diagnostic breakdown of classifier performance at the class level.
Example use cases
Auditing a release-note tagger
“I trained a classifier on changelog entries that tags each one as feature, fix, documentation, or maintenance. I've got 80 records with both actual and predicted labels — can you compute per-label precision and recall so I know which categories it's getting wrong?”
Comparing two classifier versions
“I have 150 labelled release notes from our last sprint and I want to see per-label precision and recall broken down by feature, fix, documentation, and maintenance — this is for the v2 model so I can compare against v1 results.”
Detecting recall gaps in maintenance tagging
“Our release classifier seems to miss maintenance items a lot. Can you run per-label precision and recall on these 60 labelled records — actual vs predicted across feature, fix, documentation, and maintenance — so I can confirm whether recall for maintenance is the real problem?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-release-change-per-label?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, per-label precision and recall metrics for a four-class release-change text classifier without standing up your own evaluation pipeline. It is ideal for lightweight, one-off evaluations of up to 200 records and when the label set is exactly: feature, fix, documentation, maintenance. Use it over general ML platforms when cost, setup time, and simplicity are priorities.
Side effects
- More than 200 records supplied — exceeds the bounded deliverable limit
- Missing required fields (id, text, actual, or predicted) in any record
- Invalid label value outside the allowed enum (feature, fix, documentation, maintenance)
- Empty records array — no data to evaluate
- Payment failure via x402 protocol
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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