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convention-ml-explainability

Document feature importance, SHAP, and partial dependence for machine learning models.

Updated Mar 19, 2026
Search Tags:#governance#visualization#interpretability#shap#feature-importance#ml explainability#partial-dependence
One-click install
npx skills add https://github.com/sunLeee/optimization --skill convention-ml-explainability
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: convention-ml-explainability
Source: https://github.com/sunLeee/optimization/tree/main/.claude/skills/reference/convention/convention-ml-explainability
Command: npx skills add https://github.com/sunLeee/optimization --skill convention-ml-explainability

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about convention-ml-explainability

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I explain machine learning models to business stakeholders?▼

To explain machine learning models to business stakeholders, you apply standardized conventions documenting feature importance, SHAP explanations, and partial dependence plots. This makes model results interpretable and satisfies regulatory governance needs.

What is the best way to use SHAP and feature importance for ML interpretability?▼

The best way to use SHAP and feature importance for ML interpretability is to follow model-agnostic guidance demonstrating these techniques across tree-based, linear, and neural models. Standardized workflows ensure explanations are consistently communicated to non-technical audiences.

How do I document partial dependence plots for regulatory governance needs?▼

You document partial dependence plots for regulatory governance needs by enforcing YAML frontmatter metadata and standardized conventions within your ML project. This ensures model explanations satisfy compliance requirements across product decisions.

Does this ML explainability convention work with tree-based and neural models?▼

Yes, this ML explainability convention works with tree-based and neural models. It provides model-agnostic guidance covering interpretability techniques like feature importance, SHAP, and attention visualization across various model types.

What are the limitations of model-agnostic guidance for interpretability?▼

A limitation of model-agnostic guidance for interpretability is that it provides generalized conventions rather than model-specific optimizations. You should supplement it with specialized attention visualization for complex neural models to ensure accurate explanations.

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