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Language Routing Confidence Calibration Bins
x402
Language Routing 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 2026-10-15).
Computes confidence calibration bins for a language routing classifier given up to 200 labelled text records with actual labels, predicted labels, and class probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Language routing - Confidence calibration bins: Confidence calibration bins for Language routing. 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 confidence calibration bins summarizing how well the classifier's predicted probabilities align with actual accuracy across the confidence spectrum — including bin boundaries, sample counts per bin, mean confidence per bin, and observed accuracy per bin, enabling identification of over- or under-confidence.
Example use cases
Diagnosing overconfident language classifier
“My language detection model seems overconfident — the predicted probabilities are often near 0.9 but accuracy is lower. Can you run confidence calibration bins on these 80 labelled records so I can see where the model is miscalibrated? Each record has the actual label, predicted label, and probabilities for english, french, spanish, and other.”
Pre-deployment calibration audit
“Before we ship this language routing system, I need to verify its confidence scores are trustworthy. I have 200 labelled examples with actual and predicted language labels and their probability vectors — please compute calibration bins and show me how accuracy tracks with confidence across the bins.”
Comparing two language model variants
“I'm evaluating two versions of my language router and want calibration metrics for the first one. Here are 120 records with actual labels (english, french, spanish, other), predicted labels, and the four-class probabilities — can you generate the confidence calibration bins for this batch?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-language-routing-calibration?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to evaluate the calibration quality of a 4-class language routing model (english/french/spanish/other) using your own labelled dataset. It is ideal for offline evaluation workflows, pre-deployment audits, and debugging overconfident or underconfident classifiers without needing a hosted model or foundation-model training infrastructure. At $0.01 per call it is cost-effective for iterative calibration checks on batches up to 200 records.
Side effects
- Fewer than required records provided causing insufficient bin population
- Probabilities array does not sum to 1 or contains values outside [0,1]
- Record missing required fields (id, text, actual, predicted, probabilities)
- More than 200 records submitted exceeding the per-call limit
- Invalid enum value for actual or predicted language label
- Probabilities array does not have exactly 4 elements
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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