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Task Type Grouped Three-Fold Baseline Evaluation
x402
Task Type 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 2026-10-15).
Runs a grouped three-fold cross-validation using a naive-Bayes classifier on up to 200 labelled text records categorized by task type (research, writing, analysis, implementation).
Price / call$0.01
Success—
StatusUnknown
How this service works
Task type - Grouped three-fold baseline evaluation: Grouped three-fold baseline evaluation for Task type. 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 a grouped three-fold cross-validation report including fold-level and aggregate accuracy metrics from a naive-Bayes baseline classifier trained and evaluated on the supplied labelled text records, with no hosted model or foundation-model involvement.
Example use cases
Validate task annotation consistency
“I've labelled 120 text snippets as either research, writing, analysis, or implementation tasks. Can you run a grouped three-fold cross-validation baseline on them to see if the labels are consistent and learnable?”
Pre-model dataset quality check
“Before I spend money training a real classifier, can you run a cheap naive-Bayes baseline evaluation on my 80 task-type labelled records to see if the signal is there?”
Compare task categories separability
“I have 150 documents labelled across research, writing, analysis, and implementation — run a grouped three-fold baseline evaluation and tell me the per-fold accuracy so I can see which categories the model struggles with.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-task-type-cross-validation?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, low-cost ($0.01 USDC) sanity-check baseline on a small labelled text dataset for task-type classification (research, writing, analysis, implementation) before committing to heavier model training. It is ideal when you want grouped cross-validation that respects record grouping structure, and when a naive-Bayes local baseline is sufficient to assess label quality or dataset separability without calling a foundation model.
Side effects
- Fewer than 2 records supplied — minimum input not met
- Records missing required fields (id, text, actual) — schema validation error
- Label values outside allowed enum (research, writing, analysis, implementation) — rejected
- More than 200 records supplied — exceeds maximum batch size
- Insufficient class representation per fold — may produce degenerate evaluation results
- Text field empty or too short to yield meaningful features — low-quality baseline output
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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