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Project Status Fixed-Holdout Learning Curve
x402
Project Status 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 project-status text dataset using a naive-Bayes classifier, showing how model accuracy changes with training set size.
Price / call$0.01
Success—
StatusUnknown
How this service works
Project status - Fixed-holdout learning curve: Fixed-holdout learning curve for Project status. 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
Returns a fixed-holdout learning curve deliverable: a series of accuracy or performance metric values computed at increasing training set sizes using a naive-Bayes baseline on the supplied labelled text records, with the holdout evaluation set held constant throughout.
Example use cases
Sample size sufficiency check for PM tool
“I have 120 labelled project update notes tagged as planned, active, blocked, or complete. Can you run a fixed-holdout learning curve on them so I can see whether collecting more labels would meaningfully improve classification accuracy?”
Baseline model benchmark before scaling
“Before I invest in a larger model, I want to benchmark a naive-Bayes classifier on my 60 project status records — run a fixed-holdout learning curve and show me how accuracy evolves with training set size across the planned, active, blocked, and complete categories.”
Classroom exercise on learning curve shape
“I'm teaching a data science class and need a concrete learning curve example using project status text data. I have 40 labelled records split across planned, active, blocked, and complete — can you compute and return the fixed-holdout learning curve so students can see how accuracy climbs with more training examples?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-project-status-learning-curve?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, inexpensive ($0.01 USDC) learning curve evaluation for project status text classification using a naive-Bayes baseline, with up to 200 labelled records and a fixed holdout methodology. Prefer it over hosted model solutions when you want a lightweight, local-style benchmark without training a foundation model, and when your labels are specifically project-status categories (planned, active, blocked, complete).
Side effects
- Fewer than 2 records supplied — minimum item count not met
- Records missing required fields (id, text, actual) — validation error
- Label values outside allowed enum (planned, active, blocked, complete) — schema rejection
- More than 200 records supplied — exceeds maxItems limit
- Text exceeds 1000-character limit per record — truncation or rejection
- Insufficient class diversity for meaningful holdout split — degenerate curve output
- Payment not completed via x402 protocol — request not processed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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