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Release Change Prediction Error Records
x402
Release Change Prediction Error Records is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).
Computes prediction error metrics for a labelled dataset of release-change text records classified into feature/fix/documentation/maintenance categories.
Price / call$0.01
Success—
StatusUnknown
How this service works
Release change - Prediction error records: Prediction error records 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 prediction error records deliverable.
Output
A bounded deliverable of prediction error records for the submitted release-change labelled dataset, including per-record error analysis and evaluation metrics (e.g. misclassification details, log-loss components) derived from actual vs predicted labels and class probabilities, using local dataset tools and naive-Bayes baselines.
Example use cases
Audit release classifier misclassifications
“I've run my changelog classifier on 150 release notes and got back predicted labels and probabilities. Can you send these records with their actual vs predicted labels to get a full prediction error report so I can see where the model is going wrong?”
Benchmark naive-Bayes baseline on release types
“I want to evaluate a naive-Bayes baseline for classifying commits into feature, fix, documentation, or maintenance. Here are 200 labelled examples with predicted labels and probabilities — can you compute the prediction error records for this dataset?”
QA pipeline validation for release tagger
“Before shipping our automated release tagger to production, I need to check its error profile on a holdout set of 80 labelled items with actual and predicted categories and their class probability vectors. Can you run the prediction error records analysis on them?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-release-change-errors?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, local evaluation of a release-change text classifier across the four categories (feature, fix, documentation, maintenance) without invoking a hosted or foundation model. Ideal for auditing small holdout sets (up to 200 records), running naive-Bayes baselines, or diagnosing misclassifications in a changelog/commit tagger pipeline at minimal cost ($0.01 per call).
Side effects
- More than 200 records submitted — exceeds maximum batch size
- Missing required fields (id, text, actual, predicted, probabilities) on any record
- Probabilities array does not contain exactly 4 values or values outside [0,1]
- Invalid enum value for actual or predicted label (must be feature, fix, documentation, or maintenance)
- Empty records array submitted
- Payment not included or insufficient (requires 0.01 USDC via x402)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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