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Training Question Grouped Three-Fold Baseline Evaluation
x402
Training Question Grouped Three-Fold Baseline Evaluation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Runs a grouped three-fold cross-validation baseline evaluation on up to 200 labelled training question text records using a naive-Bayes classifier.
Price / call$0.01
Success—
StatusUnknown
How this service works
Training question - Grouped three-fold baseline evaluation: Grouped three-fold baseline evaluation 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 grouped three-fold baseline evaluation deliverable.
Output
A bounded grouped three-fold baseline evaluation deliverable including cross-validation accuracy and per-fold classification metrics for the supplied labelled text records across the concept, procedure, example, and assessment categories using a naive-Bayes classifier. No hosted model is trained or retained.
Example use cases
Baseline check before model training
“I've got 150 labelled training question texts — roughly 40 concepts, 35 procedures, 40 examples, and 35 assessments. Can you run a grouped three-fold cross-validation baseline on them so I know if my labels are learnable before I invest in a real model?”
Dataset quality validation for e-learning platform
“We're building an educational content tagger and want to check whether our hand-labelled dataset of 80 training question texts holds up under cross-validation — can you run the grouped three-fold naive-Bayes baseline evaluation and tell me the accuracy?”
Comparing label consistency across content types
“I have 60 training question records labelled as concept, procedure, example, or assessment and I'm worried some categories are mislabelled. Can you run the grouped three-fold baseline evaluation to see where the classifier struggles most?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-training-question-cross-validation?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, cheap ($0.01) local baseline evaluation for a small labelled text dataset (up to 200 records) classified into training question types (concept, procedure, example, assessment) without needing a hosted model or foundation model. Ideal for sanity-checking label quality, measuring dataset learnability, or generating a naive-Bayes benchmark before committing to larger model training pipelines.
Side effects
- Fewer than 2 records supplied — minimum input not met
- More than 200 records supplied — exceeds maxItems limit
- Records missing required fields (id, text, actual) — validation error
- Invalid 'actual' label value outside the allowed enum — schema rejection
- Text records exceeding 1000 characters each — field length violation
- Insufficient class representation per fold for meaningful grouped cross-validation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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