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Release Change Type Cross-Validation Baseline Evaluator
x402
Release Change Type Cross-Validation Baseline Evaluator 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).
Runs a grouped three-fold cross-validation using a naive-Bayes classifier on up to 200 labelled release change records to evaluate text classification accuracy across feature, fix, documentation, and maintenance categories.
Price / call$0.01
Success—
StatusUnknown
How this service works
Release change - Grouped three-fold baseline evaluation: Grouped three-fold baseline evaluation 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 grouped three-fold baseline evaluation deliverable.
Output
A grouped three-fold cross-validation result using a naive-Bayes baseline classifier, including per-fold and aggregate accuracy or performance metrics across the four release change categories (feature, fix, documentation, maintenance), delivered as a single bounded evaluation report.
Example use cases
Changelog classifier quality check
“I have 80 labelled release notes — each marked as either feature, fix, documentation, or maintenance — can you run a three-fold cross-validation on them and tell me the baseline accuracy I can expect from a naive-Bayes model?”
Pre-training dataset validation
“Before I invest in training a bigger model on my release change dataset, can you do a quick grouped three-fold baseline evaluation on my 150 labelled records to see if the categories are actually learnable?”
Team sprint retrospective on label quality
“Our team manually labelled 60 commit messages as feature, fix, documentation, or maintenance — run a baseline cross-validation to see if the labels are consistent enough that even a simple model can learn them.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-release-change-cross-validation?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, cheap ($0.01 USDC) sanity-check on whether a small set of labelled release change texts (up to 200 records) is classifiable using a simple naive-Bayes model with grouped three-fold cross-validation — without needing a hosted model, foundation model, or external ML infrastructure. It is ideal for label quality validation or quick baseline benchmarking before committing to more expensive model training.
Side effects
- Fewer than 2 records supplied causing a validation error
- Records missing required fields (id, text, actual) returning a 400 error
- More than 200 records supplied exceeding the maximum and being rejected
- Imbalanced classes with very few samples per category leading to unreliable fold metrics
- Insufficient class representation across folds preventing proper grouped splitting
- Payment not fulfilled via x402 protocol returning a 402 error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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