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Product Category Confidence Calibration Bins
x402
Product Category Confidence Calibration Bins 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 confidence calibration bins for a product category classifier (apparel, home, electronics, other) given up to 200 labelled prediction records with class probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Product category - Confidence calibration bins: Confidence calibration bins 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 confidence calibration bins deliverable.
Output
Returns confidence calibration bins for the product category classifier — bin boundaries, mean predicted probability per bin, actual accuracy per bin, and sample counts — enabling reliability diagram construction and identification of over- or under-confidence regions.
Example use cases
Audit overconfident electronics classifier
“I've got 120 labelled predictions from my product classifier — texts tagged as apparel, home, electronics, or other — along with the predicted label and 4-class probability scores for each. Can you compute the confidence calibration bins so I can see where the model is overconfident?”
Validate model before production deployment
“Before I ship this product category model to production, I need to check its calibration. I have 180 records with actual labels, predicted labels, and probability vectors for the four classes. Run the calibration bin analysis and tell me how well the confidence scores match real accuracy.”
Compare calibration across dataset slices
“I have two batches of 90 labelled product records each — one batch is all apparel items, the other is mixed home and electronics. Can you run the confidence calibration bin computation on each batch so I can compare how calibrated the model is across those slices?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-product-category-calibration?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need calibration bin statistics specifically for a 4-class product category classifier (apparel, home, electronics, other) and have up to 200 labelled prediction records with probability vectors. It is a lightweight, pay-per-call option suitable for spot-checking model calibration without setting up local evaluation infrastructure or training a new model.
Side effects
- More than 200 records supplied — request rejected or truncated
- Probability arrays not summing to 1 or containing values outside [0,1] — validation error
- Fewer than 4 probability values per record — schema validation failure
- Unknown class label outside enum (apparel, home, electronics, other) — rejected
- Empty records array — insufficient data error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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