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Product Category Label and Prediction Audit
x402
Product Category Label and Prediction Audit 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).
Audits up to 200 labelled text records for a product category classifier (apparel, home, electronics, other), computing evaluation metrics and naive-Bayes baselines.
Price / call$0.01
Success—
StatusUnknown
How this service works
Product category - Label and prediction audit: Label and prediction audit 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 label and prediction audit deliverable.
Output
A bounded audit deliverable covering: label-vs-prediction agreement statistics, per-class accuracy and error breakdown, confusion analysis across apparel/home/electronics/other, probability calibration insights, and a naive-Bayes baseline comparison for the supplied dataset of up to 200 records.
Example use cases
E-commerce classifier quality check
“I've got 200 product listings where I recorded the actual category (apparel, home, electronics, or other), what my classifier predicted, and the probability scores for each class. Can you run a label and prediction audit and tell me where it's going wrong?”
Identifying misclassified apparel records
“My product classifier keeps mixing up apparel and home items — can you audit these 80 labelled records with their predicted labels and probabilities and show me where the confusion is happening compared to a naive-Bayes baseline?”
Pre-deployment model evaluation
“Before I ship this product category model to production, can you run an audit on these 120 sample records — I have the ground-truth labels, model predictions, and four-class probability vectors for each — and give me precision, recall, and a baseline comparison?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-product-category-label-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a small labelled dataset (up to 200 records) for a four-class product category text classifier and need a local audit with evaluation metrics and a naive-Bayes baseline — without requiring a hosted model or foundation model training. Ideal for rapid pre-deployment checks, debugging classifier errors, or validating label quality on e-commerce product data.
Side effects
- More than 200 records submitted — exceeds batch limit
- Probabilities array does not contain exactly 4 values per record
- Probability values outside 0–1 range
- Missing required fields (id, text, actual, predicted, probabilities)
- actual or predicted values outside the allowed enum (apparel, home, electronics, other)
- Empty records array submitted
- Payment not received — x402 payment required before response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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