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Feedback Theme Confidence Calibration Bins
x402
Feedback Theme 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 2026-10-19).
Computes confidence calibration bins for a naive-Bayes feedback theme classifier across quality, price, delivery, and support categories using up to 200 labelled text records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feedback theme - Confidence calibration bins: Confidence calibration bins 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 confidence calibration bins deliverable.
Output
Returns confidence calibration bin data for the submitted labelled dataset: probability ranges are grouped into bins, and within each bin the endpoint reports the mean predicted confidence versus the observed accuracy, enabling reliability diagram construction and detection of over- or under-confidence in the classifier.
Example use cases
Evaluating feedback classifier reliability
“I've trained a naive-Bayes classifier to tag customer feedback into quality, price, delivery, or support — can you run calibration bin analysis on these 80 labelled examples with their predicted probabilities so I can see if the model's confidence scores actually reflect its accuracy?”
Detecting overconfidence in support ticket triage
“My support ticket classifier always gives very high probabilities but I'm not sure those scores are meaningful — can you compute confidence calibration bins from these 150 records with actual labels, predicted labels, and 4-class probability vectors to check if it's overconfident?”
Pre-deployment model validation for feedback routing
“Before we deploy this feedback routing model, I want to validate its probability calibration — I have 200 labelled feedback samples with quality/price/delivery/support ground-truth labels and the model's predicted probabilities for each class. Can you produce the calibration bins report?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-feedback-theme-calibration?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to evaluate the probability calibration of a 4-class feedback theme classifier (quality, price, delivery, support) using a small local dataset of up to 200 labelled examples. It is ideal for post-hoc model evaluation without requiring a hosted model or foundation model. Prefer it over generic ML APIs when you specifically need calibration bin statistics rather than raw accuracy metrics, and when your classifier is a naive-Bayes or similar baseline model on customer feedback categories.
Side effects
- Fewer than the minimum required records submitted — returns error indicating insufficient data
- Probability arrays not summing to 1 or containing values outside [0,1] — validation error
- Mismatched enum values in actual/predicted fields — schema rejection
- More than 200 records submitted — truncation or rejection depending on implementation
- Empty records array — error with no bins returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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