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Support Intent Confidence Calibration Bins
x402
Support Intent Confidence Calibration Bins 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 confidence calibration bins for a support-intent classifier across billing, technical, account, and other categories using up to 200 labelled text records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Support intent - Confidence calibration bins: Confidence calibration bins for Support intent. 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 confidence calibration bins deliverable.
Output
Returns a bounded confidence calibration bins deliverable: binned probability intervals with per-bin accuracy statistics, showing how closely predicted confidence scores match observed accuracy rates across the billing, technical, account, and other support intent classes.
Example use cases
Validating support router model calibration
“I've trained a support ticket routing model and want to check if the probabilities it outputs for billing, technical, account, and other categories are actually calibrated — can you compute the confidence calibration bins using these 150 labelled records with their actual labels, predicted labels, and probabilities?”
Diagnosing overconfidence in intent classifier
“My naive-Bayes support intent classifier seems overconfident — it predicts 0.9+ probability but gets a lot wrong. Can you run calibration bins on these 200 records (each has the text, actual class, predicted class, and a 4-element probability array) so I can see where the bins diverge from perfect calibration?”
Pre-deployment QA on support ML pipeline
“Before we ship our new support intent model to production, I need a confidence calibration bins report on 180 test records labeled across billing, technical, account, and other — here are the record IDs, texts, actual labels, predicted labels, and predicted probabilities for each.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-support-intent-calibration?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to evaluate the probability calibration of a support-intent text classifier across the specific billing, technical, account, and other categories, especially with small local datasets (up to 200 records) and no need for a hosted model. Prefer it over generic ML evaluation APIs when you specifically need binned calibration output for support-domain intent classification.
Side effects
- Fewer than the minimum required records supplied — returns error or empty bins
- Probabilities array does not sum to 1 or contains values outside [0,1] — validation error
- More than 200 records supplied — input rejected or truncated
- Class labels not from allowed enum (billing, technical, account, other) — schema validation error
- Insufficient records per bin — sparse or unreliable calibration bin output
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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