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Review Aspect Grouped Three-Fold Baseline Evaluation
x402
Review Aspect 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 2026-10-12).
Runs a grouped three-fold cross-validation using a naive-Bayes classifier on up to 200 labelled review-aspect text records (performance, appearance, value, other) and returns baseline evaluation metrics.
Price / call$0.01
Success—
StatusUnknown
How this service works
Review aspect - Grouped three-fold baseline evaluation: Grouped three-fold baseline evaluation for Review aspect. 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 naive-Bayes baseline model trained on the supplied labelled records, including per-fold and aggregate accuracy or classification metrics across the four review-aspect classes (performance, appearance, value, other). No hosted model is created; output is a one-time bounded evaluation deliverable.
Example use cases
Baseline check before model training
“Before I invest in training a proper model, can you run a naive-Bayes three-fold cross-validation on these 60 labelled product reviews to see if the categories — performance, appearance, value, and other — are separable at all?”
QA on a newly labelled review dataset
“I just finished labelling 150 customer reviews by aspect (performance, appearance, value, other). Can you run a grouped three-fold baseline evaluation on them so I can see how consistent the labels are and what accuracy a simple classifier gets?”
Comparing aspect labelling across two annotators
“I have 80 review texts labelled by aspect — performance, appearance, value, and other. Run the grouped three-fold baseline evaluation on them and tell me the accuracy and any fold-level breakdowns so I can spot where the labels are noisy.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-review-aspect-cross-validation?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a small labelled dataset (up to 200 records) of review-aspect text and need a quick, cheap ($0.01) baseline evaluation using grouped three-fold cross-validation with a naive-Bayes model. Ideal for validating label quality, sanity-checking class separability, or establishing a baseline before investing in larger model training. Not suitable if you need a hosted model, deep-learning embeddings, or evaluation on more than 200 records.
Side effects
- Fewer than 2 records supplied — minimum input requirement not met
- Records missing required fields (id, text, actual) — schema validation failure
- Text field exceeds 1000 characters per record — truncation or rejection
- More than 200 records supplied — exceeds maximum item limit
- Invalid enum value in 'actual' field (must be performance, appearance, value, or other)
- Insufficient class representation across folds for meaningful cross-validation — may produce degenerate metrics
- Payment failure via x402 protocol — endpoint returns 402 without processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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