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Feedback Theme Cross-Validation — Grouped Three-Fold Baseline Evaluation
x402
Feedback Theme 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 2026-10-19).
Runs a grouped three-fold cross-validation with a naive-Bayes baseline classifier on up to 200 labelled feedback text records across four themes: quality, price, delivery, and support.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feedback theme - Grouped three-fold baseline evaluation: Grouped three-fold baseline evaluation for Feedback theme. 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 containing cross-validation accuracy metrics computed by a naive-Bayes classifier over the supplied labelled text records. Results indicate how well the four feedback themes (quality, price, delivery, support) can be separated by a simple baseline model, with per-fold or aggregate performance scores.
Example use cases
Benchmark feedback classifier before training
“I've got 120 labelled customer reviews tagged as either quality, price, delivery, or support. Can you run a grouped three-fold baseline evaluation on them so I know how well a naive-Bayes model can separate those categories before I invest in anything heavier?”
Validate labelling quality of feedback dataset
“We just finished manually labelling 180 customer support tickets into quality, price, delivery, and support themes — can you run a three-fold cross-validation with a naive-Bayes baseline to check if the labels are consistent and learnable?”
Compare theme separability across feedback channels
“I have 90 product review snippets split across quality, price, delivery, and support labels. Run a grouped three-fold baseline evaluation on them so I can see which themes are easiest for a simple model to distinguish and which ones are getting confused.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-feedback-theme-cross-validation?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a small labelled feedback dataset (up to 200 records) and want a quick, cheap ($0.01 USDC) baseline evaluation using a naive-Bayes classifier with grouped three-fold cross-validation, without needing a hosted model or foundation-model infrastructure. It is specifically designed for the four feedback themes — quality, price, delivery, support — and is ideal for validating label quality, estimating baseline separability, or benchmarking before committing to more expensive ML pipelines.
Side effects
- Fewer than 2 records supplied — minItems constraint violated
- More than 200 records supplied — maxItems constraint exceeded
- Invalid or missing 'actual' label — must be one of: quality, price, delivery, support
- Record ID exceeds 80 characters or is empty — minLength/maxLength constraint violated
- Text field exceeds 1000 characters per record
- Insufficient class representation across folds for meaningful cross-validation
- Payment not completed via x402 protocol — request rejected before processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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