model-evaluation
CommunityAssess ML models with robust metrics and fairness checks.
Authorcosmix
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill guides evaluating machine learning models with appropriate metrics, validation strategies, and monitoring for production reliability.
Core Features & Use Cases
- Metric Selection: Accuracy, precision, recall, F1, ROC-AUC, etc.
- Validation & Fairness: Cross-validation, distribution shift tests, and bias checks.
- Monitoring: Report dashboards and alerting for production drift.
Quick Start
Evaluate a binary classifier on a test split and generate a confusion matrix plus ROC curve.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 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: model-evaluation Download link: https://github.com/cosmix/claude-code-setup/archive/main.zip#model-evaluation Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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