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Ticket Priority Multiclass Brier Score
x402
Ticket Priority 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 2026-10-15).
Computes the multiclass Brier score for a ticket priority classifier (low/normal/high) from up to 200 labelled records with predicted probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Ticket priority - Multiclass Brier score: Multiclass Brier score for Ticket priority. 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 value summarising probabilistic prediction quality across up to 200 ticket records labelled as low, normal, or high priority. Lower scores indicate better-calibrated probability estimates.
Example use cases
Calibration check before production deploy
“Before I push my ticket triage model to production, can you compute the multiclass Brier score on these 120 labelled tickets? Each record has the actual priority, the predicted label, and the three class probabilities.”
A/B comparison of two triage models
“I'm comparing two ticket priority classifiers — here are 80 labelled test records with probabilities from Model B. Compute the Brier score so I can compare it against Model A's score.”
Weekly classifier health check
“Run the multiclass Brier score on this week's 200 resolved tickets where I have the actual priority, my model's predicted label, and the probability outputs — I want to track whether calibration is drifting.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-ticket-priority-brier?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a single probabilistic calibration metric for a three-class ticket priority model (low/normal/high) and have up to 200 labelled records with per-class probabilities. It is purpose-built for this specific classification schema and costs only $0.01 per evaluation, making it ideal for lightweight, repeated model health checks without running a full ML pipeline locally.
Side effects
- Fewer than 2 records supplied — minimum record count not met
- Probability array does not have exactly 3 elements per record
- Probability values outside [0, 1] range
- Actual or predicted label not one of 'low', 'normal', 'high'
- More than 200 records submitted — exceeds maximum
- Payment of 0.01 USDC not fulfilled — x402 payment required
- Malformed JSON body — missing required fields id, text, actual, predicted, or probabilities
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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