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Feedback Theme Label and Prediction Audit
x402
Feedback Theme 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 2026-10-19).
Audits up to 200 labelled text records for a 4-class feedback theme classifier (quality, price, delivery, support), computing evaluation metrics from supplied actual and predicted labels with class probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feedback theme - Label and prediction audit: Label and prediction audit for Feedback theme. 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 label and prediction audit deliverable covering the supplied records: includes classification evaluation metrics (such as per-class accuracy, confusion breakdown, and probability-based assessments) computed locally against the supplied actual and predicted labels using small naive-Bayes baselines. No hosted or foundation model training is performed.
Example use cases
Post-deployment classifier accuracy check
“We just deployed a feedback theme model and I want to audit its predictions on 120 labelled records — actual labels are quality, price, delivery, or support, and I have the predicted labels plus probability scores for each class. Can you run a full prediction audit and tell me where it's going wrong?”
Comparing model outputs before release
“I have 80 customer feedback records with ground-truth labels and two sets of predictions from our model candidates. Start with the first set — actual and predicted labels are one of quality, price, delivery, or support, and I have four class probabilities per record. Run a label audit so I can see which performs better.”
Diagnosing low-confidence misclassifications
“My feedback classifier seems to be confusing 'delivery' and 'support' categories. I've got 200 labelled records with actual labels, predicted labels, and four-element probability arrays for each. Can you do a prediction audit so I can see the misclassification patterns and probability calibration?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-feedback-theme-label-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, low-cost ($0.01 USDC) audit of a pre-existing 4-class feedback theme classifier — specifically when you already have predicted labels and class probabilities and want evaluation metrics computed against ground truth, without needing to train or host a model. It is ideal for quick post-deployment validation, regression checks, or benchmarking on up to 200 records where the classes are quality, price, delivery, and support.
Side effects
- More than 200 records submitted — batch size limit exceeded
- Probabilities array does not contain exactly 4 elements per record
- Label values outside the allowed enum (quality, price, delivery, support)
- Record ID missing or exceeds 80-character limit
- Malformed JSON body or missing required fields causing rejection
- Text field exceeds 1000-character limit per record
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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