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Task Type Multiclass Brier Score
x402
Task Type Multiclass Brier Score 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 text classifier predicting task types (research, writing, analysis, implementation) from up to 200 labelled records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Task type - Multiclass Brier score: Multiclass Brier score for Task type. 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 value for the submitted labelled records, quantifying the mean squared error between the predicted probability vectors and the one-hot actual class vectors across the four task-type categories (research, writing, analysis, implementation). Lower scores indicate better-calibrated probabilistic predictions.
Example use cases
Calibration check for NLP pipeline
“I've trained a naive-Bayes classifier that assigns documents to research, writing, analysis, or implementation. Can you calculate the multiclass Brier score on these 50 labelled examples, including the predicted probabilities for each class, so I can see how well-calibrated it is?”
Comparing two task classifiers
“I have outputs from two different task-type models — both predict research, writing, analysis, or implementation — each with 80 labelled records and probability vectors. Can you give me the Brier score for the first batch so I can compare it against the second?”
Pre-deployment classifier quality gate
“Before I ship my document routing model, can you evaluate its multiclass Brier score on 120 held-out records? Each record has the actual task type (research, writing, analysis, or implementation), the model's predicted label, and the four predicted probabilities summing to 1.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-task-type-brier?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a proper scoring rule evaluation (Brier score) specifically for a 4-class task-type text classifier (research/writing/analysis/implementation) on small to medium labelled datasets (up to 200 records) and want a cheap, fast, bounded metric without setting up your own evaluation infrastructure.
Side effects
- Fewer than 1 or more than 200 records submitted — request rejected
- Probability vector does not have exactly 4 elements or values outside [0,1] — validation error
- Missing required fields (id, text, actual, predicted, probabilities) on any record — schema error
- actual or predicted label not in enum (research, writing, analysis, implementation) — enum validation failure
- Payment of 0.01 USDC not included or x402 protocol error — payment failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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