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Review Aspect Prediction Error Records
x402
Review Aspect 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 review aspect classifier (performance/appearance/value/other) given up to 200 labelled text records with predicted probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Review aspect - Prediction error records: Prediction error records for Review aspect. 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 up to 200 supplied labelled text instances, detailing which records were misclassified, the actual vs predicted aspect labels (performance/appearance/value/other), and associated probability scores to support local model evaluation and error analysis.
Example use cases
Audit classifier mistakes on review dataset
“I've got 120 labelled review snippets for my aspect classifier — each one has the true label (performance, appearance, value, or other), the model's predicted label, and the four class probability scores. Can you compute the prediction error records so I can see exactly which records the model got wrong?”
Compare model variants on shared test set
“I'm comparing two versions of my review aspect model and I want to see which records version B gets wrong. Here are 80 labelled records with their actual labels, predicted labels, and probability arrays — please run the prediction error records analysis and give me the full error breakdown.”
Debug low-confidence misclassifications
“My review aspect classifier seems to confuse 'value' and 'performance' reviews a lot. I have 200 labelled records with actual and predicted aspect labels and probability scores — can you compute the prediction error records so I can identify the systematic misclassification patterns?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-review-aspect-errors?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, local evaluation of a review aspect text classifier (covering performance, appearance, value, and other categories) without needing a hosted model or foundation-model fine-tuning. It is ideal for small-batch auditing (up to 200 records), debugging naive-Bayes or similar baselines, and producing bounded error records cheaply at $0.01 USDC per call.
Side effects
- More than 200 records submitted — request rejected or truncated
- Probabilities array does not contain exactly 4 values — validation error
- Probabilities values outside [0,1] range — validation error
- Missing required fields (id, text, actual, predicted, probabilities) — schema validation failure
- Invalid enum value for actual or predicted (not one of performance/appearance/value/other) — error
- Payment not processed — 402 response, no deliverable returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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