calibration-sparse-bin-protection
CommunityEnhance confidence calibration analysis by protecting against sparse bins
Authorruskibeats
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill addresses the issue of sparse bins in confidence calibration analysis, ensuring accurate Expected Calibration Error (ECE) calculations and reliable threshold recommendations for detector confidence scores.
Core Features & Use Cases
- Sparse Bin Protection: Automatically merges low-sample bins into nearest neighbors, maintaining calibration curve integrity.
- Min-Sample Floor for Thresholds: Ensures threshold recommendations are based on robust sample sizes.
- Reporting: Provides a detailed report on merged bins for transparency.
- Use Case: For developers calibrating detector confidence scores from limited evaluation samples, this Skill helps maintain the reliability of calibration results.
Quick Start
Use the calibration-sparse-bin-protection skill to analyze calibration curves from your dataset and generate a report on bin merging and ECE.
Dependency Matrix
Required Modules
numpyscipy
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: calibration-sparse-bin-protection Download link: https://github.com/ruskibeats/t1d/archive/main.zip#calibration-sparse-bin-protection Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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