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Product Category Fixed-Holdout Learning Curve
x402
Product Category 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 product category text classification (apparel, home, electronics, other) using a naive-Bayes baseline on up to 200 labelled records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Product category - Fixed-holdout learning curve: Fixed-holdout learning curve for Product category. 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
Returns a fixed-holdout learning curve report showing how a naive-Bayes baseline classifier's performance metrics (such as accuracy or F1) vary as the number of training records increases, evaluated against a held-out portion of the supplied labelled dataset spanning categories: apparel, home, electronics, and other.
Example use cases
Benchmarking e-commerce classifier before scaling
“I have 120 labelled product descriptions split across apparel, home, electronics, and other — can you run a fixed-holdout learning curve so I can see how the naive-Bayes accuracy improves as training size increases, before I invest in a bigger model?”
Estimating data needs for a product tagger
“We're building a product category tagger and only have 60 labelled text samples. Can you compute a learning curve using a fixed holdout so we can figure out roughly how many more labelled records we'd need to hit a decent accuracy?”
QA check on a new product taxonomy dataset
“We just annotated 150 product listings as apparel, home, electronics, or other — run a fixed-holdout learning curve on them so we can sanity-check whether the labels are consistent and see how quickly the baseline improves.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-product-category-learning-curve?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, inexpensive learning curve evaluation for product category text classification using a local naive-Bayes baseline, especially when you have a small labelled dataset (up to 200 records) and want to understand how model performance scales with training size — without deploying a hosted model or fine-tuning a foundation model. Ideal for early-stage data quality checks, dataset sizing decisions, and lightweight ML benchmarking on the four product categories: apparel, home, electronics, and other.
Side effects
- Fewer than 2 records supplied — minimum input not met
- Record text exceeds 1000-character limit — truncation or rejection
- Invalid or missing 'actual' label not in allowed enum — validation error
- Fewer than one example per class — class imbalance may prevent curve generation
- Dataset too small to produce meaningful holdout split — degenerate curve output
- Payment not processed — x402 payment required before response is returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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