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Multiclass Brier Score Calculator for Training Questions
x402
Multiclass Brier Score Calculator for Training Questions is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Computes the multiclass Brier score for up to 200 labelled text records classified into concept, procedure, example, or assessment categories
Price / call$0.01
Success—
StatusUnknown
How this service works
Training question - Multiclass Brier score: Multiclass Brier score for Training question. 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 bounded multiclass Brier score deliverable — a numerical score (lower is better, 0 = perfect) representing the mean squared error between predicted class probability vectors and one-hot encoded actual labels across all submitted records.
Example use cases
Evaluating educational content classifier calibration
“I've built a naive Bayes classifier that sorts training materials into concept, procedure, example, or assessment. Can you calculate the multiclass Brier score for these 80 labelled records I have — each one has the actual label, predicted label, and four predicted probabilities?”
Comparing model iterations on held-out data
“I want to see if my updated text classifier is better calibrated than before. Can you compute the Brier score across these 120 records with their actual categories, predicted categories, and probability vectors so I can compare it to my previous run?”
Quality check before deploying a question tagger
“Before I go live with my question-tagging model, can you run a Brier score evaluation on these 200 sample records — each has the true label (concept, procedure, example, or assessment), what the model predicted, and the four class probabilities?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-training-question-brier?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a probabilistic calibration metric (Brier score) for a four-class text classification problem with labels concept, procedure, example, and assessment — particularly for training question datasets. It is purpose-built for this exact label schema and handles up to 200 records per call at minimal cost, making it ideal for iterative model evaluation without needing hosted model infrastructure.
Side effects
- Probabilities array does not contain exactly 4 elements per record
- Probabilities do not sum to approximately 1.0
- More than 200 records submitted in one call
- Invalid or missing actual/predicted class labels outside allowed enum values
- Empty records array provided
- Record ID exceeds 80 character limit
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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