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Knowledge Topic Fixed-Holdout Learning Curve
x402
Knowledge Topic 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).
Computes a fixed-holdout learning curve for a knowledge-topic text classifier (setup/usage/troubleshooting/reference) using a naive-Bayes baseline on up to 200 labelled records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Knowledge topic - Fixed-holdout learning curve: Fixed-holdout learning curve for Knowledge topic. 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 report showing classifier accuracy (and related metrics) at incremental training set sizes, produced by a local naive-Bayes baseline model evaluated on a held-out split of the supplied labelled text records. No hosted or foundation model is involved.
Example use cases
Evaluate documentation classifier dataset adequacy
“I have 150 labelled support docs tagged as setup, usage, troubleshooting, or reference. Can you run a fixed-holdout learning curve on them so I can see whether collecting more training data would meaningfully improve classification accuracy?”
Benchmark knowledge base topic model before deployment
“Before we go live with our knowledge-base topic classifier, can you compute a learning curve using these 200 labelled articles — each with an id, text up to 1000 characters, and one of the four topic labels — to see how the naive-Bayes baseline performs across training sizes?”
Diagnose low-accuracy knowledge topic classifier
“Our topic classifier for knowledge articles is performing poorly and I'm not sure if the problem is too little data or noisy labels. Can you run a fixed-holdout learning curve on my 80 labelled records (setup, usage, troubleshooting, reference) to help diagnose where accuracy stalls?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-knowledge-topic-learning-curve?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 labelled text records across the four knowledge-topic categories (setup, usage, troubleshooting, reference) and want a lightweight, low-cost learning curve evaluation using a local naive-Bayes baseline — without needing to train or host a foundation model. Ideal for quickly diagnosing whether more labelled data is needed before investing in larger model training.
Side effects
- Fewer than 2 records supplied — minimum input not met
- Records missing required fields (id, text, actual) — schema validation error
- More than 200 records submitted — exceeds maximum input limit
- Label value outside allowed enum (setup/usage/troubleshooting/reference) — invalid label error
- Too few examples per class for a meaningful holdout split — degenerate curve output
- Payment not included or insufficient — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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