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Product Category Classifier — Prediction Error Records
x402
Product Category Classifier — Prediction Error Records 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).
Computes prediction error records (e.g. misclassification details, per-record loss) for a naive-Bayes product category text classifier across apparel, home, electronics, and other categories.
Price / call$0.01
Success—
StatusUnknown
How this service works
Product category - Prediction error records: Prediction error records 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 prediction error records deliverable.
Output
A bounded deliverable containing prediction error records for each submitted labelled text record — detailing which records were mispredicted, the actual versus predicted category labels (apparel, home, electronics, other), and associated probability information, computed using local naive-Bayes evaluation tools without a hosted model.
Example use cases
Auditing apparel misclassifications
“I have 150 labelled product descriptions with actual and predicted categories plus probability scores — run them through the prediction error analysis and tell me which apparel items were predicted as something else.”
Model debugging for ecommerce team
“Our naive-Bayes product classifier is underperforming; here are 200 records with their actual labels (apparel, home, electronics, other), predicted labels, and probabilities — give me the full prediction error records so we can see where it's going wrong.”
Pre-launch classifier validation
“Before we ship this product categorization model, I want to check its errors on our 80-record validation set — each record has an ID, text, actual category, predicted category, and four class probabilities. Can you produce the prediction error records for me?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-product-category-errors?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need per-record prediction error diagnostics for a four-class product category text classifier (apparel, home, electronics, other) and already have labelled data with predicted outputs and class probabilities. It is ideal for model evaluation and debugging pipelines where you supply your own predictions and want structured error records rather than aggregate metrics. Prefer alternatives for aggregate metrics (precision/recall), multi-class problems beyond these four categories, or when you need a hosted model to generate predictions rather than just evaluate them.
Side effects
- Records array exceeds 200 items — request will be rejected or truncated
- Probabilities array does not contain exactly 4 values — validation error
- Probability values outside [0,1] range — schema validation failure
- Missing required fields (id, text, actual, predicted, probabilities) — 400 error
- Invalid category enum value not in [apparel, home, electronics, other] — schema rejection
- Empty records array — may return empty or error response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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