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Training Question Label and Prediction Audit
x402
Training Question 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 2026-10-24).
Audits labels and model predictions for up to 200 text records classified into training question categories (concept, procedure, example, assessment), returning performance metrics and error analysis.
Price / call$0.01
Success—
StatusUnknown
How this service works
Training question - Label and prediction audit: Label and prediction audit for Training question. 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
Returns a bounded label and prediction audit deliverable covering accuracy metrics, per-class performance breakdown, and misclassification analysis for up to 200 supplied records across the four training question categories (concept, procedure, example, assessment). Includes comparison of actual vs predicted labels and probability-based diagnostics.
Example use cases
Evaluating educational content classifier
“I have 120 labelled training question records where each text is tagged as concept, procedure, example, or assessment, and my naive Bayes model made predictions with probabilities for each class — can you run a label and prediction audit so I can see per-class accuracy and which categories are getting confused?”
Identifying mislabelled training data
“Run a prediction audit on these 80 training question records — I want to find examples where the actual label disagrees with the predicted label so I can clean up my training dataset before retraining.”
Benchmarking classifier before deployment
“Before I deploy my training question classifier, can you audit 200 of its predictions against the ground-truth labels and give me a breakdown of where it struggles — especially between procedure and example categories?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-training-question-label-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, low-cost audit of classifier predictions on educational/training question text without needing a hosted model or large-scale infrastructure. Best suited for small batches (up to 200 records), naive Bayes or similar small classifiers, and when you want a quick evaluation report for concept/procedure/example/assessment classification. Use when you have pre-computed predictions and probabilities and need an independent audit rather than retraining.
Side effects
- Records array exceeds 200 items — request rejected or truncated
- Missing required fields (id, text, actual, predicted, probabilities) in any record — validation error
- Probabilities array does not contain exactly 4 values or values fall outside [0,1] — schema error
- Invalid enum value for actual or predicted (must be concept, procedure, example, or assessment) — validation error
- Empty records array — returns empty or minimal audit report
- id field exceeds 80 characters — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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