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Review Aspect Label and Prediction Audit
x402
Review Aspect 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 classifier labels and predictions for review aspect classification (performance, appearance, value, other) across up to 200 labelled text records, computing evaluation metrics using naive-Bayes baselines.
Price / call$0.01
Success—
StatusUnknown
How this service works
Review aspect - Label and prediction audit: Label and prediction audit 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 label and prediction audit deliverable.
Output
A bounded audit deliverable covering classification accuracy and error analysis for up to 200 review records labelled across four aspect categories (performance, appearance, value, other). Includes evaluation metrics comparing predicted vs actual labels, probability-based diagnostics, and naive-Bayes baseline comparisons computed locally without hosted model inference.
Example use cases
E-commerce classifier quality check
“I've got 120 product reviews tagged with actual aspect categories — performance, appearance, value, or other — and my model's predicted labels and probabilities for each. Can you audit the predictions and tell me which aspect categories the model struggles with most?”
Model regression testing after retraining
“We just retrained our review aspect classifier and I want to audit 180 labelled test records — each has the actual aspect, the new model's predicted aspect, and all four class probabilities. Run a prediction audit so I can compare the new model's error profile to baseline.”
Data quality review before model training
“Before we finalize our training dataset, can you audit these 75 labelled review records for aspect classification? I have actual labels, predicted labels from a quick model, and class probability vectors for each record — I want to catch any labelling inconsistencies or systematic errors.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-review-aspect-label-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, low-cost ($0.01) audit of a review aspect classifier's label quality and prediction accuracy on a small labelled dataset (up to 200 records) with four fixed categories (performance, appearance, value, other). Ideal for validating classifier outputs, catching labelling errors, or benchmarking against a naive-Bayes baseline without needing hosted model infrastructure or foundation model access.
Side effects
- Exceeding 200 records per call returns an error or truncated results
- Malformed probability arrays (not exactly 4 values summing near 1.0) may cause computation errors
- Invalid or out-of-enum actual/predicted labels will fail schema validation
- Empty records array produces no meaningful audit output
- Missing required fields (id, text, actual, predicted, probabilities) per record causes rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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