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Product Category Multiclass Brier Score
x402
Product Category Multiclass Brier Score 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 the multiclass Brier score for product category text classification predictions across apparel, home, electronics, and other classes.
Price / call$0.01
Success—
StatusUnknown
How this service works
Product category - Multiclass Brier score: Multiclass Brier score 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 multiclass brier score deliverable.
Output
Returns a bounded multiclass Brier score value for the submitted product category predictions, quantifying how well the predicted class probabilities match the true one-hot encoded labels. Lower scores indicate better calibration. The result covers up to 200 labeled records across the four product categories: apparel, home, electronics, and other.
Example use cases
E-commerce classifier calibration check
“I just trained a naive Bayes classifier to categorize product listings into apparel, home, electronics, or other. I have 120 labeled examples with their actual categories, predicted categories, and the four probability scores for each. Can you run the multiclass Brier score on them so I can see how calibrated my model's confidence is?”
A/B comparison of two product classifiers
“I'm comparing two product category models and want to pick the better-calibrated one. Can you compute the multiclass Brier score for my first model's predictions? I've got 80 records with actual labels (apparel, home, electronics, other), predicted labels, and probability arrays for each.”
Pre-deployment model quality gate
“Before I push my product categorization model to production, I need to check its probability calibration on a holdout set of 150 text records across the apparel, home, electronics, and other classes. Can you calculate the multiclass Brier score for me using the actual labels, predicted labels, and class probabilities I have?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-product-category-brier?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, inexpensive ($0.01 USDC) evaluation of probability calibration for a text classifier operating over the specific product categories of apparel, home, electronics, and other. It is ideal for lightweight local evaluation without access to a hosted ML platform, or when you want a standardized Brier score deliverable for up to 200 records without setting up your own evaluation pipeline.
Side effects
- Probability arrays not summing to 1 or containing values outside [0,1] may cause validation errors
- Records missing required fields (id, text, actual, predicted, probabilities) will be rejected
- Submitting more than 200 records may exceed the allowed limit and result in an error
- Invalid actual or predicted category values outside the four allowed enums will fail schema validation
- Empty records array returns no meaningful score
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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