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Knowledge Topic Classifier – Confidence Calibration Bins
x402
Knowledge Topic Classifier – 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 2026-10-14).
Computes confidence calibration bins for a naive-Bayes knowledge-topic classifier across up to 200 labelled text records with actual labels, predicted labels, and class probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Knowledge topic - Confidence calibration bins: Confidence calibration bins for Knowledge topic. 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 set of confidence calibration bins summarizing how well the classifier's predicted probabilities align with observed accuracy across the four knowledge-topic classes (setup, usage, troubleshooting, reference). Each bin groups records by predicted confidence level and reports the mean confidence and actual accuracy within that bin, enabling reliability-diagram analysis and detection of over- or under-confidence.
Example use cases
Auditing classifier overconfidence before deployment
“Before we ship our knowledge-base topic classifier, I want to check whether its confidence scores are actually calibrated — here are 150 labelled records with actual and predicted categories plus their four class probabilities. Can you produce the confidence calibration bins so I can see where the model is overconfident?”
Comparing two model versions' calibration
“I've got predictions from two rounds of my naive-Bayes knowledge topic model and I want to compare their calibration quality. Start with this first batch of 120 records — actual labels are setup/usage/troubleshooting/reference, I have the predicted labels and probabilities for each. Give me the calibration bins so I can benchmark the baseline.”
Diagnosing poor reliability on troubleshooting class
“My support-article classifier seems to be poorly calibrated specifically on the troubleshooting category. I have 80 labelled examples with actual and predicted labels and probability vectors — can you run the confidence calibration bins analysis so I can pinpoint where the predicted probabilities are diverging from real accuracy?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-knowledge-topic-calibration?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need calibration bin metrics specifically for a four-class knowledge-topic classifier (setup, usage, troubleshooting, reference) and have up to 200 labelled records with class probability vectors available. It is ideal for post-training evaluation of naive-Bayes or similar local models where you want to assess probability reliability without sending data to a hosted foundation model. Prefer it over generic calibration libraries when you need a pay-per-call, infrastructure-free evaluation deliverable in an agentic workflow.
Side effects
- Too many records supplied (over 200 items) — request rejected or truncated
- Probability vectors not summing to 1 or containing values outside [0,1] — validation error
- Mismatched enum values in actual/predicted fields (values outside setup/usage/troubleshooting/reference) — schema validation failure
- Fewer than 4 probability values per record (minItems:4 not met) — schema error
- Empty records array — returns empty or degenerate calibration output
- Payment not processed (x402 protocol failure) — 402 response with no result
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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