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Language Routing Multiclass Brier Score Evaluator
x402
Language Routing Multiclass Brier Score Evaluator 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 the multiclass Brier score for a language routing classifier given up to 200 labelled text records with predicted probability distributions.
Price / call$0.01
Success—
StatusUnknown
How this service works
Language routing - Multiclass Brier score: Multiclass Brier score for Language routing. 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 scalar derived from the supplied labelled records and their predicted probability vectors, quantifying the mean squared error between the predicted probability distributions and the one-hot actual labels across english, french, spanish, and other classes.
Example use cases
Calibration check for language router
“I just trained a language routing model that classifies text into english, french, spanish, or other. Can you calculate the multiclass Brier score on these 80 labelled test records — each has the actual language, predicted label, and a probability vector — so I can see how well-calibrated the predictions are?”
Comparing two language classifier versions
“I want to compare two versions of my language detector. Can you compute the Brier score for version A's predictions? I have 120 labelled records with actual labels, predicted labels, and the 4-class probability distributions for english, french, spanish, and other.”
QA audit of production language routing
“Before we push our language routing model to production, can you run a Brier score evaluation on a held-out set of 200 records? Each record has the document text, the true language label, the model's predicted label, and the four class probabilities for english, french, spanish, and other.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-language-routing-brier?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a calibration-sensitive multiclass metric (Brier score) specifically for a language routing classifier over english, french, spanish, and other classes, with up to 200 labelled records per call. Prefer it over log-loss endpoints when you want a bounded, squared-error measure of probabilistic calibration rather than a logarithmic penalty. Use it for quick offline evaluation of small held-out sets without requiring a hosted model.
Side effects
- More than 200 records supplied — request exceeds batch limit
- Probability vectors with fewer or more than 4 elements — schema validation failure
- Probabilities outside [0,1] range — validation error
- Invalid or unrecognised class label in actual or predicted fields — enum validation failure
- Empty records array — no data to evaluate
- Malformed JSON body — request rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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