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Support Intent Fixed-Holdout Learning Curve
x402
Support Intent 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 2026-10-12).
Generates a fixed-holdout learning curve for a naive-Bayes support intent classifier (billing/technical/account/other) using up to 200 labelled text records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Support intent - Fixed-holdout learning curve: Fixed-holdout learning curve for Support intent. 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
A fixed-holdout learning curve report showing classifier accuracy (or related metrics) at successive training set sizes, computed using a local naive-Bayes baseline on the supplied labelled records, with a fixed holdout split for consistent evaluation across training increments.
Example use cases
Support routing model data sizing
“I have 180 labelled customer support messages tagged as billing, technical, account, or other. Run a fixed-holdout learning curve on them so I can see whether I need more data before deploying a classifier.”
Comparing intent categories by difficulty
“Can you run a naive-Bayes learning curve on my 120 support ticket records — each labelled billing, technical, account, or other — and tell me which intent categories are hardest to learn with few examples?”
Validating a new support dataset quality
“I just labelled 90 customer support texts across billing, technical, account, and other intents. Run a fixed-holdout learning curve to check if the labels are consistent and the dataset is learnable.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-support-intent-learning-curve?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, cheap diagnostic on whether a small labelled support-intent dataset is learnable and how much data is needed — without deploying a hosted or foundation model. Ideal for dataset sizing experiments, label quality checks, and naive-Bayes baselines on support ticket classification with up to 200 examples.
Side effects
- Fewer than 2 records supplied (minItems violation)
- More than 200 records supplied (maxItems violation)
- Record text exceeds 1000 characters
- Record ID missing or empty
- Invalid 'actual' label outside enum (billing/technical/account/other)
- Insufficient class diversity for holdout split
- Payment of 0.01 USDC not processed via x402 protocol
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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