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Return Reason Classifier – Fixed-Holdout Learning Curve
x402
Return Reason Classifier – Fixed-Holdout Learning Curve 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).
Generates a fixed-holdout learning curve for a naive-Bayes return-reason text classifier using up to 200 user-supplied labelled records, without any hosted or foundation-model training.
Price / call$0.01
Success—
StatusUnknown
How this service works
Return reason - Fixed-holdout learning curve: Fixed-holdout learning curve for Return reason. 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 fixed-holdout learning curve deliverable.
Output
A fixed-holdout learning curve deliverable showing how a naive-Bayes text classifier's performance metrics (e.g. accuracy) vary as the training set size increases, evaluated against a held-out portion of the supplied labelled records. Includes local baseline evaluation results without any hosted model or foundation-model training.
Example use cases
Labelled data sufficiency check for returns
“I have 120 labelled customer return messages tagged as damaged, wrong_item, unwanted, or other — can you run a fixed-holdout learning curve so I can see if collecting more labels would meaningfully improve classification accuracy?”
Naive-Bayes baseline benchmark for returns team
“Our returns team tagged 200 customer complaint texts with one of four reasons: damaged, wrong_item, unwanted, or other. Run a learning curve analysis with a fixed holdout so we can benchmark how a simple naive-Bayes model performs across different training sizes.”
Early-stage dataset quality assessment
“We only have about 40 labelled return reason examples split across damaged, wrong_item, unwanted, and other — can you compute a fixed-holdout learning curve to see whether the data quality and volume are good enough to train a basic classifier?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-learning-curve?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a small labelled dataset (up to 200 records) of customer return reason texts and want a quick, low-cost learning curve analysis using a naive-Bayes baseline — particularly when you don't need a hosted model or foundation-model fine-tuning, and just want to understand how classifier performance scales with training data size for the four standard return reason categories.
Side effects
- Fewer than 2 records supplied — minimum input violation
- Records missing required fields (id, text, actual) — schema validation error
- More than 200 records submitted — maxItems constraint exceeded
- Invalid 'actual' enum value (not one of: damaged, wrong_item, unwanted, other) — rejected
- Insufficient class diversity in records may produce degenerate learning curves
- Payment failure via x402 protocol — endpoint returns 402 status
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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