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Knowledge Topic Label and Prediction Audit
x402
Knowledge Topic 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 2026-10-16).
Audits classifier labels and predictions for knowledge-topic text records (setup, usage, troubleshooting, reference) using local naive-Bayes evaluation tools.
Price / call$0.01
Success—
StatusUnknown
How this service works
Knowledge topic - Label and prediction audit: Label and prediction audit 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 label and prediction audit deliverable.
Output
A bounded label and prediction audit deliverable covering up to 200 text records. Includes classification accuracy metrics, per-class performance breakdown, confusion analysis between setup/usage/troubleshooting/reference categories, naive-Bayes baseline comparison, and identification of misclassified records — produced using local dataset evaluation tools.
Example use cases
Documentation classifier accuracy review
“I have 150 labelled knowledge articles where each one is tagged as setup, usage, troubleshooting, or reference. My classifier has also predicted a label and output four class probabilities for each. Can you audit the predictions against the actual labels and tell me where my model is going wrong?”
Naive-Bayes baseline benchmarking
“Run a label and prediction audit on my 80 knowledge-topic text records — I want to see how my classifier's predictions compare to a naive-Bayes baseline across the four categories: setup, usage, troubleshooting, and reference.”
Knowledge base triage quality check
“Before I deploy my knowledge topic classifier, I want to validate its output on a sample of 200 records. Each record has the document text, actual label, predicted label, and predicted probabilities for setup, usage, troubleshooting, and reference. Can you audit the label quality and flag where predictions diverge from actual?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-knowledge-topic-label-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, low-cost ($0.01) audit of a knowledge-topic text classifier across the four categories (setup, usage, troubleshooting, reference) without requiring a hosted model or foundation-model training infrastructure. Ideal for validating small to mid-sized labelled datasets (up to 200 records) using naive-Bayes baselines and local evaluation tools. Prefer this over hosted ML evaluation services when you need a quick, cost-effective sanity check on classifier quality for documentation or knowledge management systems.
Side effects
- More than 200 records submitted — input exceeds maximum batch size
- Missing required fields (id, text, actual, predicted, probabilities) in one or more records
- Probabilities array does not contain exactly 4 values or values outside [0,1] range
- Invalid class label not in enum (setup, usage, troubleshooting, reference)
- Payment not completed or insufficient USDC balance for x402 transaction
- Empty records array submitted
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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