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Release Change Fixed-Holdout Learning Curve
x402
Release Change Fixed-Holdout Learning Curve 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).
Generates a fixed-holdout learning curve for a labelled dataset of release change text records (feature, fix, documentation, maintenance) using a local naive-Bayes classifier.
Price / call$0.01
Success—
StatusUnknown
How this service works
Release change - Fixed-holdout learning curve: Fixed-holdout learning curve 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 fixed-holdout learning curve deliverable.
Output
A fixed-holdout learning curve report showing how naive-Bayes classification performance (e.g. accuracy or F1) on the held-out portion of the supplied labelled records varies as the training set size increases, computed entirely locally without any hosted or foundation model.
Example use cases
Benchmarking changelog classifier data needs
“I have 150 labelled changelog entries tagged as feature, fix, documentation, or maintenance — can you run a fixed-holdout learning curve so I can see how many labelled examples I actually need before accuracy plateaus?”
Evaluating release note triage model
“We're building a tool to auto-triage release notes into feature, fix, documentation, and maintenance buckets. Can you run a learning curve on our 120 labelled samples so we know whether our current dataset size is sufficient?”
Comparing baseline performance across data volumes
“Before investing in a bigger labelling effort, I want to know how a naive-Bayes model performs on 60 of our release change records as training size increases — can you generate the fixed-holdout learning curve for me?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-release-change-learning-curve?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, cheap learning curve analysis on a small labelled release change dataset (up to 200 records) using a transparent naive-Bayes baseline, without sending data to a hosted or foundation model. Ideal for gauging how much more labelled data is needed before investing in heavier ML infrastructure, or for auditable offline-style evaluation within an agentic pipeline.
Side effects
- Fewer than 2 records supplied — minimum input not met
- Records missing required fields (id, text, actual) — validation error
- More than 200 records supplied — exceeds maximum limit
- Invalid label value outside allowed enum (feature, fix, documentation, maintenance)
- Insufficient class diversity in records to form a meaningful holdout split
- Payment of 0.01 USDC not completed — request rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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