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Ticket Priority Label and Prediction Audit
x402
Ticket Priority Label and Prediction Audit is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).
Audits labeled ticket priority predictions (low/normal/high) against actuals for up to 200 records, returning evaluation metrics and naive-Bayes baseline comparisons.
Price / call$0.01
Success—
StatusUnknown
How this service works
Ticket priority - Label and prediction audit: Label and prediction audit 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 label and prediction audit deliverable.
Output
Returns a bounded audit report covering prediction accuracy, per-class metrics (precision, recall, F1), confusion matrix analysis, probability calibration assessment, and comparison against a naive-Bayes baseline for the supplied labeled ticket records.
Example use cases
Support ticket model accuracy check
“I've got 120 support tickets where my model predicted low, normal, or high priority — I have the actual priorities and the predicted probabilities for each. Can you run a prediction audit to see where my classifier is going wrong and give me accuracy metrics?”
Pre-deployment classifier validation
“Before we ship our ticket triage model to production, audit these 80 labeled ticket records with their predicted priorities and probability scores against the actual labels — I need to know if we're beating a naive Bayes baseline or not.”
Identifying mislabeled training data
“I suspect some of my ticket priority labels are noisy. Can you run a label and prediction audit on these 200 tickets — I'll give you each ticket's text, what the actual priority label is, what my model predicted, and the class probabilities — and flag where the labels look inconsistent?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-ticket-priority-label-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, low-cost audit of a ticket priority classifier's predictions against ground truth labels without provisioning infrastructure or calling a foundation model. It is ideal for spot-checking model quality, validating training data labels, or benchmarking against a naive-Bayes baseline on small-to-medium datasets (up to 200 records). Prefer alternatives if you need to train or fine-tune a model, handle more than 200 records, or require deep semantic analysis.
Side effects
- Fewer than 2 records provided returns a validation error
- Records missing required fields (id, text, actual, predicted, probabilities) cause schema rejection
- Probability arrays not containing exactly 3 values are rejected
- More than 200 records submitted exceeds the maximum and is rejected
- Invalid label values outside low/normal/high enum cause a validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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