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Task Type Prediction Error Records
x402
Task Type Prediction Error Records 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).
Computes prediction error records for a naive-Bayes task-type classifier (research/writing/analysis/implementation) given up to 200 labelled text records with predicted labels and class probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Task type - Prediction error records: Prediction error records 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 prediction error records deliverable.
Output
A bounded deliverable containing prediction error records for the submitted labelled text records — identifying which records were misclassified, the actual vs predicted task-type labels, and associated probability information, produced via local naive-Bayes evaluation (no hosted model or foundation-model training).
Example use cases
Diagnosing classifier confusion between task types
“I trained a naive-Bayes classifier to label tasks as research, writing, analysis, or implementation. I have 80 labelled records with actual labels, predicted labels, and probability scores. Can you compute the prediction error records so I can see exactly where the model is making mistakes?”
QA audit of task labelling pipeline
“We're auditing our automated task labelling pipeline before going to production. Can you run prediction error records on these 120 labelled documents — I have the actual task types, predicted task types, and the four-class probability vectors for each one — and give me the bounded error deliverable?”
Evaluating small dataset classifier on task categories
“I've got a small test set of 30 text records tagged with actual and predicted task types (research, writing, analysis, implementation) plus the class probabilities. Can you compute the prediction error records to evaluate how well the model is doing?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-task-type-errors?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a cheap, bounded error-record report for a small labelled dataset (up to 200 records) classified into the four task types (research, writing, analysis, implementation) using a local naive-Bayes baseline — particularly useful for quick evaluation audits without needing a hosted model or foundation-model infrastructure.
Side effects
- Records array exceeds 200 items — request rejected
- Probabilities array does not contain exactly 4 values per record — validation error
- Probability values outside [0,1] range — schema validation failure
- Missing required fields (id, text, actual, predicted, probabilities) — rejected
- Invalid label value outside enum (research, writing, analysis, implementation) — schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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