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Product Category Confusion Matrix
x402
Product Category Confusion Matrix 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).
Computes a confusion matrix for a product category classifier (apparel, home, electronics, other) from up to 200 labeled text records with actual and predicted labels.
Price / call$0.01
Success—
StatusUnknown
How this service works
Product category - Confusion matrix: Confusion matrix 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 confusion matrix deliverable.
Output
A bounded confusion matrix deliverable showing counts of true positives, false positives, false negatives, and true negatives across the four product categories (apparel, home, electronics, other), summarizing where the classifier's predictions agree or diverge from actual labels.
Example use cases
Audit ecommerce classifier errors
“I ran my product text classifier over 120 items from our catalog and recorded the actual versus predicted categories for apparel, home, electronics, and other — can you build me a confusion matrix so I know which categories it's mixing up?”
Compare two product taggers
“I've got 80 product descriptions each with a ground-truth category and a label from our new tagger — please compute the confusion matrix across apparel, home, electronics, and other so I can see the error patterns before we ship it.”
Evaluate naive-Bayes product model
“We trained a naive-Bayes model on product titles and here are the actual vs predicted labels for 60 test items across the four categories — compute the confusion matrix so we can measure how often it misclassifies home goods as other.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-product-category-confusion?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need a fast, inexpensive confusion matrix computation for a 4-class product category classifier (apparel, home, electronics, other) on a small labeled dataset of up to 200 records, without needing to train or host a model. Prefer this over general ML platforms when you want a single bounded deliverable at minimal cost via micropayment.
Side effects
- More than 200 records supplied — request rejected or truncated
- Invalid or missing required fields (id, text, actual, predicted) on any record
- Actual or predicted values outside the allowed enum (apparel, home, electronics, other)
- Empty records array returns a trivial or error response
- Payment of 0.01 USDC not completed causes request rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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