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Fixed-Holdout Learning Curve for Training Question Classification
x402
Fixed-Holdout Learning Curve for Training Question Classification 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).
Generates a fixed-holdout learning curve analysis for a naive-Bayes text classifier trained to categorize training content as concept, procedure, example, or assessment.
Price / call$0.01
Success—
StatusUnknown
How this service works
Training question - Fixed-holdout learning curve: Fixed-holdout learning curve for Training question. 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 showing how naive-Bayes classifier accuracy on training question categories (concept, procedure, example, assessment) varies with training set size, computed from up to 200 supplied labelled text records using local evaluation tools and no hosted or foundation model.
Example use cases
Assess classifier readiness before scaling
“I have 120 labelled training questions tagged as concept, procedure, example, or assessment — can you run a fixed-holdout learning curve so I can see whether adding more data would meaningfully improve my naive-Bayes classifier?”
Validate dataset balance for e-learning platform
“Here are 150 labelled pieces of educational content — concepts, procedures, examples, and assessments — can you generate a learning curve with a fixed holdout so I can tell if my dataset is large enough to train a reliable classifier?”
Benchmark text classifier for curriculum tagging
“I'm building a curriculum tagging tool and have 60 manually labelled documents split across concept, procedure, example, and assessment. Run a fixed-holdout learning curve to show me how accuracy scales with training set size.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-training-question-learning-curve?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 labelled text records in the training question domain (concept/procedure/example/assessment) and need a quick, cheap ($0.01 USDC) learning curve evaluation using a naive-Bayes baseline without requiring a hosted model or expensive foundation model API. Best suited for feasibility checks, dataset sizing decisions, and lightweight ML benchmarking on educational content classification tasks.
Side effects
- Fewer than 2 records supplied — minimum record count not met
- Records missing required fields (id, text, actual) — validation error
- Class label not in allowed enum (concept, procedure, example, assessment) — rejected record
- More than 200 records supplied — exceeds maximum input size
- Insufficient class representation to form meaningful holdout split — degenerate curve
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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