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Knowledge Topic Cross-Validation - Grouped Three-Fold Baseline Evaluation
x402
Knowledge Topic Cross-Validation - 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 2026-10-16).
Runs a grouped three-fold cross-validation using a naive-Bayes classifier on up to 200 labelled knowledge-topic text records (setup, usage, troubleshooting, reference) and returns baseline accuracy metrics.
Price / call$0.01
Success—
StatusUnknown
How this service works
Knowledge topic - Grouped three-fold baseline evaluation: Grouped three-fold baseline evaluation for Knowledge topic. 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
Returns grouped three-fold cross-validation results from a local naive-Bayes classifier trained on the supplied labelled records. Output includes per-fold and aggregate accuracy or classification metrics across the four knowledge-topic categories (setup, usage, troubleshooting, reference), giving a baseline benchmark without any hosted model or foundation-model training.
Example use cases
Baseline accuracy check for documentation classifier
“I have 120 labelled help-center articles tagged as setup, usage, troubleshooting, or reference. Can you run a naive-Bayes grouped three-fold evaluation on them and tell me what baseline accuracy I can expect before building a more sophisticated classifier?”
Label quality validation for new knowledge base
“We just labelled 80 internal wiki pages as setup, usage, troubleshooting, or reference — can you evaluate how clean and separable those labels are by running a three-fold baseline cross-validation on the texts?”
Pre-training feasibility check for topic router
“Before we invest in fine-tuning a model to route support tickets into setup, usage, troubleshooting, or reference buckets, run a cheap naive-Bayes baseline on our 200 labelled examples so we can see if the categories are even learnable from text alone.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-knowledge-topic-cross-validation?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, cheap ($0.01) baseline evaluation of a knowledge-topic text classification task using a naive-Bayes model and three-fold cross-validation, without requiring a hosted model, API key, or foundation-model fine-tuning. It is ideal for validating label quality, estimating class separability, or benchmarking prior to investing in a more complex ML pipeline. Prefer it over hosted ML evaluation services when data privacy is a concern (local evaluation), when cost is a constraint, or when the dataset is small (up to 200 records) and you only need a simple baseline.
Side effects
- Fewer than 2 records supplied — minimum input not met
- Records missing required fields (id, text, actual) — schema validation error
- More than 200 records submitted — exceeds maxItems limit
- Invalid label value in 'actual' field — must be one of: setup, usage, troubleshooting, reference
- Text too long — individual text fields exceeding 1000 characters may be rejected
- Insufficient class representation for three-fold grouping — too few examples per class to form valid folds
- Payment not processed — x402 payment of 0.01 USDC required per call
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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