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Ticket Priority Label Distribution
x402
Ticket Priority Label Distribution 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 label distribution (class balance) across low, normal, and high priority labels in a supplied set of up to 200 labelled ticket text records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Ticket priority - Label distribution: Label distribution 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 distribution deliverable.
Output
A bounded label distribution report showing the count and/or proportion of records assigned to each ticket priority class (low, normal, high) across the supplied dataset of up to 200 labelled text records.
Example use cases
Pre-training class imbalance check
“Before I train a ticket priority classifier, I need to check whether my 120 labelled tickets are balanced across low, normal, and high priority. Can you analyze the label distribution so I know if I need to oversample any class?”
Support team dataset audit
“We have 80 recently labelled support tickets marked as low, normal, or high priority — can you give me a breakdown of how many fall into each category so I can report on labelling consistency to my team?”
Naive-Bayes baseline dataset prep
“I'm setting up a naive-Bayes baseline for ticket priority classification and want to see the label balance in my 200-record dataset before I proceed. Can you compute the distribution of low, normal, and high labels?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-ticket-priority-label-balance?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, low-cost label distribution summary for a ticket priority dataset with up to 200 records, especially before training or evaluating a classifier and wanting to diagnose class imbalance. It is ideal for small dataset audits where no hosted model or foundation-model training is needed.
Side effects
- Fewer than 2 records supplied — minimum input requirement not met
- More than 200 records supplied — exceeds maximum item limit
- Missing required fields (id, text, or actual) on any record
- Invalid enum value for 'actual' field (must be low, normal, or high)
- Record ID exceeds 80 characters or text exceeds 1000 characters
- Empty or malformed request body
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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