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Return Reason Multiclass Brier Score Calculator
x402
Return Reason Multiclass Brier Score Calculator is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-14).
Computes the multiclass Brier score for a return-reason classifier given up to 200 labelled text records with predicted probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Return reason - Multiclass Brier score: Multiclass Brier score for Return reason. 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 single multiclass Brier score (a float between 0 and 2, where 0 is perfect) summarising the mean squared error between predicted probability vectors and one-hot actual labels across all supplied records and the four return-reason classes (damaged, wrong_item, unwanted, other).
Example use cases
Calibration check after model retrain
“I just retrained my return-reason classifier and I have 150 labelled validation records with actual labels across damaged, wrong_item, unwanted, and other, plus the 4-class probability outputs. Can you compute the multiclass Brier score so I can compare calibration before and after retraining?”
A/B comparison of two classifiers
“I'm comparing two models that predict return reasons. I have 80 test records with actual labels and probability vectors from model A — please compute the multiclass Brier score for model A so I can benchmark it against model B.”
Quality gate before production deploy
“Before we ship this return-reason model to production, run a multiclass Brier score on these 200 labelled records with the model's predicted probabilities across damaged, wrong_item, unwanted, and other — flag it if the score looks too high to deploy.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-brier?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a cheap, fast, bounded multiclass Brier score specifically for the four return-reason classes (damaged, wrong_item, unwanted, other) without standing up your own evaluation infrastructure. It is ideal for quick calibration checks, CI/CD quality gates, or comparing model versions on small batches of up to 200 records. It is not suitable for large-scale offline batch evaluation, arbitrary class taxonomies, or use cases requiring full confusion matrices or other metrics beyond the Brier score.
Side effects
- More than 200 records supplied — request rejected or truncated
- Probability vectors that do not sum to 1 or contain values outside [0,1] — validation error
- Mismatched enum value in actual/predicted fields — schema validation error
- Empty records array — returns error or degenerate score
- Fewer than 4 probabilities in a probability vector — schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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