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Project Status Confidence Calibration Bins
x402
Project Status 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 project-status classifier, showing how well predicted probabilities align with actual outcomes across planned/active/blocked/complete labels.
Price / call$0.01
Success—
StatusUnknown
How this service works
Project status - Confidence calibration bins: Confidence calibration bins for Project status. 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
A set of confidence calibration bins showing, for each probability range, how the model's predicted confidence compares to actual accuracy across the four project status classes (planned, active, blocked, complete). Useful for identifying over- or under-confidence patterns in the classifier.
Example use cases
Diagnosing overconfidence in project classifier
“My project status model keeps predicting 'active' with 90%+ confidence but it's wrong a lot — can you run confidence calibration bins on these 150 records with their actual labels, predicted labels, and probability scores so I can see where the model is miscalibrated?”
Pre-deployment model quality check
“Before we ship this project status classifier, I need to verify its probability calibration. Here are 200 labelled records with actual and predicted statuses (planned, active, blocked, complete) and the four class probabilities — can you give me the calibration bins?”
Comparing two classifier versions
“I retrained my project status model and want to know if the new version is better calibrated. Can you calculate calibration bins for this set of 180 records with their actual labels, predicted labels, and class probabilities for planned, active, blocked, and complete?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-project-status-calibration?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need calibration bin analysis specifically for a four-class project status classifier (planned/active/blocked/complete) with up to 200 labelled records. It is purpose-built for evaluating probability calibration in this domain without requiring a hosted model, making it ideal for offline evaluation workflows and audits of small-to-medium classifiers.
Side effects
- More than 200 records supplied — input is capped at 200
- Probability arrays not summing to 1 or missing required 4 elements — validation error
- Invalid or missing actual/predicted label enum values — schema rejection
- Empty records array — no calibration bins can be computed
- Malformed record IDs or text fields exceeding length limits
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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