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Ticket Priority Confidence Calibration Bins
x402
Ticket Priority Confidence Calibration Bins 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 confidence calibration bins for a ticket priority classifier using up to 200 labelled text records with predicted probabilities and actual labels.
Price / call$0.01
Success—
StatusUnknown
How this service works
Ticket priority - Confidence calibration bins: Confidence calibration bins 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 confidence calibration bins deliverable.
Output
Returns confidence calibration bins for the ticket priority classifier, showing how predicted probabilities align with actual outcome frequencies across bins — enabling diagnosis of overconfidence, underconfidence, and reliability of probability estimates for low, normal, and high priority classes.
Example use cases
Diagnose overconfident ticket triage model
“My support ticket priority classifier seems overconfident — it always predicts 0.95+ probability but gets it wrong a lot. Can you run calibration bins on these 120 labelled tickets with their actual vs predicted priorities and probability scores so I can see where the miscalibration is worst?”
Pre-deployment model quality check
“Before we ship our new ticket priority model to production, run a confidence calibration analysis on this set of 80 validation tickets — each has the actual priority label, what the model predicted, and the full 3-class probability vector.”
Compare classifier calibration after retraining
“We just retrained our ticket priority classifier. Can you compute the calibration bins for 200 of our recent tickets that have actual labels, predicted labels, and probability outputs, so we can see if calibration improved compared to our last baseline?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-ticket-priority-calibration?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to evaluate the calibration quality of a ticket priority text classifier that outputs 3-class (low/normal/high) probabilities. It is ideal for model validation workflows, post-training audits, and diagnosing trust in predicted confidence scores. Best suited for small-to-medium evaluation sets up to 200 records using local naive-Bayes baseline comparisons without requiring hosted model infrastructure.
Side effects
- Fewer than 2 records supplied — minimum input requirement not met
- Probability arrays not summing correctly or containing values outside [0,1]
- Actual or predicted labels outside allowed enum values (low, normal, high)
- More than 200 records supplied — exceeds maximum limit
- Malformed record structure missing required fields (id, text, actual, predicted, probabilities)
- Payment failure — insufficient USDC balance or x402 payment not processed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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