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Ticket Priority Fixed-Holdout Learning Curve
x402
Ticket Priority Fixed-Holdout Learning Curve 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).
Generates a fixed-holdout learning curve for a ticket priority classifier (low/normal/high) using up to 200 labelled text records and a naive-Bayes baseline.
Price / call$0.01
Success—
StatusUnknown
How this service works
Ticket priority - Fixed-holdout learning curve: Fixed-holdout learning curve 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 fixed-holdout learning curve deliverable.
Output
Returns a fixed-holdout learning curve deliverable showing how classification performance (accuracy or related metrics) changes as the number of training examples increases, computed using a naive-Bayes baseline on the supplied labelled ticket text records split with a fixed holdout. Includes evaluation metrics across training-size increments.
Example use cases
Assess data sufficiency for ticket triage
“I have 150 labelled support tickets tagged as low, normal, or high priority — can you run a fixed-holdout learning curve so I can see whether adding more labelled examples would meaningfully improve my ticket classifier?”
Benchmark naive-Bayes on priority labels
“Run a fixed-holdout learning curve benchmark on my 80 labelled ticket records (each with an ID, text, and a low/normal/high priority label) so I can see the naive-Bayes baseline accuracy at different training sizes.”
Validate classifier before production rollout
“Before we deploy our ticket priority model, can you evaluate it with a fixed holdout split across these 200 labelled tickets and give me a learning curve showing accuracy vs training data size for the low, normal, and high classes?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-ticket-priority-learning-curve?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 labelled support ticket texts with low/normal/high priority labels and want to understand how classifier performance scales with training data size using a lightweight naive-Bayes model — no hosted model or foundation model required. Ideal for early-stage data assessment, data sufficiency analysis, and local benchmarking before committing to larger ML pipelines.
Side effects
- Fewer than 2 records supplied — minimum input not met
- Records missing required fields (id, text, actual) — validation error
- More than 200 records supplied — exceeds maxItems limit
- Invalid priority label (not low/normal/high) — enum validation failure
- Text field exceeds 1000 characters per record — truncation or rejection
- Insufficient class representation to form a meaningful holdout split
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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