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Task Type Label and Prediction Audit
x402
Task Type 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 2026-10-15).
Audits labels and predictions for a task-type classifier (research/writing/analysis/implementation) over up to 200 text records, returning evaluation metrics and naive-Bayes baselines.
Price / call$0.01
Success—
StatusUnknown
How this service works
Task type - Label and prediction audit: Label and prediction audit for Task type. 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 audit deliverable including per-class and overall accuracy metrics, a confusion matrix across the four task types (research, writing, analysis, implementation), calibration analysis based on supplied probabilities, identification of misclassified records, and a naive-Bayes baseline comparison for the supplied dataset.
Example use cases
Classifier accuracy check before deployment
“Before I ship my task-type model, can you audit these 120 labelled records — each has the actual task type (research, writing, analysis, or implementation), what the model predicted, and the probability scores — and tell me the overall accuracy and where it's making the most mistakes?”
Label quality review for training data
“I think some of my training data labels might be wrong — can you run a prediction audit on these 80 records with their actual and predicted task-type labels plus probabilities, and flag the ones where the model's confidence disagrees strongly with the supplied label?”
Baseline comparison for new task classifier
“I just built a task-type classifier and want to see how it compares to a naive-Bayes baseline. Can you run a label and prediction audit on my 200 records covering research, writing, analysis, and implementation tasks, with the actual labels, predictions, and probability arrays?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-task-type-label-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, low-cost audit of a 4-class task-type text classifier with local evaluation metrics and a naive-Bayes baseline, without requiring a hosted model or foundation-model infrastructure. Ideal for rapid sanity-checks on small-to-medium labelled datasets (up to 200 records) during development or QA pipelines.
Side effects
- Records array exceeds 200 items — request rejected or truncated
- Probabilities array does not contain exactly 4 values summing to approximately 1.0 — validation error
- Invalid enum value for actual or predicted fields (must be research, writing, analysis, or implementation) — schema rejection
- Empty records array returns no meaningful audit output
- Record text exceeds 1000 character limit — truncation or rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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