convention-ml-explainability
CommunityMake ML models transparent and trustworthy.
Data & Analytics#governance#visualization#interpretability#shap#feature-importance#ml explainability#partial-dependence
AuthorsunLeee
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Provide practical conventions and guidelines to make machine learning model results interpretable for business stakeholders.
Core Features & Use Cases
- Interpretability techniques: guidance on feature importance, SHAP, partial dependence, and attention visualization across model types.
- Standards & adoption: standardized conventions for documenting explanations and communicating results to non-technical audiences.
- Use Case: ensure model explanations satisfy regulatory and governance needs in product decisions.
Quick Start
Apply the convention to your ML project by documenting a feature-importance analysis, SHAP explanations, and a partial-dependence plot for at least one model.
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: convention-ml-explainability Download link: https://github.com/sunLeee/optimization/archive/main.zip#convention-ml-explainability Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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