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Release Change Confusion Matrix
x402
Release Change Confusion Matrix 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 a confusion matrix for release-change text classification across four categories: feature, fix, documentation, and maintenance.
Price / call$0.01
Success—
StatusUnknown
How this service works
Release change - Confusion matrix: Confusion matrix 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 confusion matrix deliverable.
Output
A bounded confusion matrix deliverable showing how predicted release-change labels (feature, fix, documentation, maintenance) align with actual labels across up to 200 supplied records, including per-class misclassification counts.
Example use cases
Evaluating a changelog classifier
“I trained a model to categorize git commit messages into feature, fix, documentation, or maintenance. I have 120 labelled examples with actual and predicted labels — can you compute a confusion matrix so I can see which categories it's mixing up?”
Comparing two model predictions on release notes
“I'm comparing two classifiers on our release notes dataset. Can you run a confusion matrix on the first model's predictions? I have 80 records with actual and predicted labels across feature, fix, documentation, and maintenance.”
Spotting misclassification patterns before deployment
“Before we ship our release-change tagging model, I want to sanity-check it. I have 150 labelled records with actual and predicted categories — feature, fix, documentation, or maintenance. Can you compute the confusion matrix so I can see where it's going wrong?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-release-change-confusion?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 labelled text records for a release-change classification task and need a quick, low-cost confusion matrix evaluation without standing up your own ML infrastructure. It is ideal for lightweight model evaluation of naive Bayes or small classifiers on software changelog categorization, especially when you need a bounded, pay-per-call evaluation at $0.01 per call.
Side effects
- Exceeds 200 records limit — request rejected or truncated
- Missing required fields (id, text, actual, predicted) on any record — validation error
- Invalid enum values for actual or predicted outside the four allowed categories — schema rejection
- Empty records array — no matrix can be computed
- Text field exceeding 1000 characters per record — field truncation or rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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