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Product Category Top-k Label Coverage
x402
Product Category Top-k Label Coverage 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 top-k label coverage metric for a product category classifier across up to 200 labelled text records with 4 classes (apparel, home, electronics, other).
Price / call$0.01
Success—
StatusUnknown
How this service works
Product category - Top-k label coverage: Top-k label coverage 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 top-k label coverage deliverable.
Output
Returns a top-k label coverage score indicating the proportion of records where the true label falls within the top-k highest-probability predictions, computed over up to 200 supplied labelled records across the four product categories (apparel, home, electronics, other).
Example use cases
E-commerce classifier quality check
“I trained a product classifier that categorizes items into apparel, home, electronics, or other. Can you calculate the top-2 label coverage across my 80 test records so I can see how often the true category falls within the model's top 2 predictions? I have the text, actual labels, predicted labels, and probabilities ready.”
Comparing model iterations on product data
“I have two versions of my product category model and I want to benchmark the newer one. Can you compute the top-3 label coverage for 120 labelled product descriptions — actual and predicted labels are apparel, home, electronics, or other — using the class probability arrays I have?”
Diagnosing misclassified product listings
“My product classifier is struggling with certain categories. Run a top-k label coverage evaluation at k=2 on these 30 mislabelled product records — I'll give you each item's text, the actual category, the predicted category, and the 4-class probabilities so we can see if the right label at least ranks in the top 2.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-product-category-top-k?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to evaluate a product category text classifier (4 classes: apparel, home, electronics, other) using the top-k label coverage metric specifically — particularly useful when your model produces ranked probability outputs and you want to measure how often the correct label appears within the top k predictions. Prefer this over accuracy metrics when your downstream system can tolerate top-k predictions rather than requiring a single best guess.
Side effects
- More than 200 records supplied — request rejected or truncated
- Probabilities array does not contain exactly 4 values per record — validation error
- k value outside 1–4 range — schema validation failure
- Missing required fields (id, text, actual, predicted, probabilities) in any record — error
- Probability values outside [0,1] range — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| k | integer | |
| records | array |
Tags
x402
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