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Release Change Classifier — Macro, Micro & Weighted Scores
x402
Release Change Classifier — Macro, Micro & Weighted Scores is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).
Computes macro, micro, and weighted aggregate classification scores for a batch of labelled release-change text records across four categories (feature, fix, documentation, maintenance).
Price / call$0.01
Success—
StatusUnknown
How this service works
Release change - Macro micro and weighted scores: Macro micro and weighted scores 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 macro micro and weighted scores deliverable.
Output
Returns macro, micro, and weighted aggregate classification scores (such as precision, recall, and F1) computed over up to 200 supplied records containing actual vs predicted labels across four release-change categories: feature, fix, documentation, and maintenance.
Example use cases
Evaluating a changelog triage model
“I trained a small classifier to categorize our Git commit messages into feature, fix, documentation, or maintenance buckets — here are 150 records with my model's predicted labels and the ground truth. Can you compute the macro, micro, and weighted scores so I can see how well it's doing?”
Comparing two classifier baselines
“I have two sets of predictions for our release notes classifier — baseline A is naive Bayes and baseline B is a rule-based model. Can you run the macro, micro, and weighted aggregate scoring on baseline A's 120 labelled records first so I can compare them?”
QA check before shipping a release tagger
“Before we ship our automated release change tagger, I need to validate it against 80 hand-labelled records across feature, fix, documentation, and maintenance classes — can you give me the macro, micro, and weighted scores to confirm it meets our quality bar?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-release-change-aggregate-scores?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, on-demand evaluation of a release change text classifier using aggregate scoring (macro, micro, weighted) without standing up your own evaluation infrastructure or calling a foundation model. Ideal for small batches (≤200 records) where local naive-Bayes baselines and standard sklearn-style metrics are sufficient and cost must stay minimal ($0.01/call).
Side effects
- Exceeding 200 records may result in rejection or truncation
- Missing required fields (id, text, actual, predicted) per record causes validation errors
- Invalid enum values for actual or predicted labels return schema errors
- Empty records array returns no meaningful metrics
- Payment failure (insufficient USDC) blocks execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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