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Cancellation Reason Label and Prediction Audit
x402
Cancellation Reason 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 cancellation-reason classifier, computing evaluation metrics such as accuracy, confusion matrices, and log-loss against four classes: price, usage, quality, and other.
Price / call$0.01
Success—
StatusUnknown
How this service works
Cancellation reason - Label and prediction audit: Label and prediction audit for Cancellation reason. 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 report covering aggregate and per-class evaluation metrics (accuracy, confusion matrix, log-loss, precision/recall) for up to 200 cancellation-reason records, comparing actual labels against predicted labels and probability outputs across the four classes: price, usage, quality, and other.
Example use cases
Post-deployment classifier quality check
“I just deployed a cancellation reason model and want to audit its predictions on 80 labelled support tickets — each has the actual reason (price, usage, quality, or other), what the model predicted, and the probability scores. Can you run the label and prediction audit so I can see where it's going wrong?”
Comparing model versions on held-out data
“I have 150 labelled cancellation records I held out for testing — actual labels are price, usage, quality, or other, and I've got the predicted labels and probability vectors from my new model. Run an audit so I can see the accuracy and per-class breakdown before I decide whether to ship this version.”
Debugging high-error cancellation category
“My cancellation classifier seems to be confusing 'price' and 'usage' reasons a lot. I have 60 labelled examples with actuals, predictions, and probabilities — can you do a label and prediction audit to confirm where the misclassifications are concentrated?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-cancellation-reason-label-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, low-cost audit of an existing cancellation-reason classifier's label and prediction quality on a small labelled dataset (up to 200 records) with the four standard classes (price, usage, quality, other), without needing to train or host a model. It is best suited for spot-checking model performance, debugging misclassifications, or benchmarking against naive-Bayes baselines in a local evaluation workflow.
Side effects
- More than 200 records supplied — request exceeds the bounded input limit
- Probability arrays not exactly length 4 — schema validation error
- Invalid class label outside price/usage/quality/other enum — rejected record
- Missing required fields (id, text, actual, predicted, probabilities) — validation failure
- Malformed probability values outside [0,1] range — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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