Which machine learning models does this skill support?

This skill is designed to work with a wide range of model types, including tree-based models (like XGBoost or Random Forest) and neural networks, by selecting the most appropriate technique like SHAP or LIME.

How does this skill help with model debugging?

By identifying which features contribute most to a specific prediction, it allows developers to spot unexpected feature interactions or data leakage that might be causing performance issues.

Does it provide visualizations for feature importance?

While the skill primarily focuses on generating the data and logic for explanations, it can be integrated with visualization plugins to produce charts and graphs of feature contributions.

Can I use this for regulatory compliance?

Yes, it provides the interpretability required to explain automated decisions in plain language, which is often a requirement for compliance frameworks like GDPR's 'right to explanation'.

ML Model Explainability Tool

Name: ML Model Explainability Tool
Author: jeremylongshore

byjeremylongshore

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883

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Data Science & ML

Interprets complex machine learning predictions and identifies key feature influences using SHAP and LIME techniques.

This skill empowers Claude to demystify 'black-box' machine learning models by providing clear, actionable insights into model behavior and prediction logic. It leverages advanced interpretability techniques like SHAP and LIME to help developers and data scientists understand feature importance, debug performance issues, and ensure algorithmic fairness. Whether you are explaining a loan rejection or identifying customer churn drivers, this skill bridges the gap between complex data outputs and human-readable explanations, facilitating better communication with stakeholders and ensuring model transparency.

Key Features

01Identification of primary feature importance and influential model drivers

02Fairness and transparency auditing for machine learning development workflows

03Stakeholder-ready summaries of complex model logic and decision paths

04Debugging of unexpected model predictions and complex feature interactions

05Automated SHAP and LIME explanation generation for various model types

06883 GitHub stars

Use Cases

01Explaining specific model predictions for regulatory or business compliance inquiries

02Identifying and mitigating bias by auditing feature influences on model outcomes

03Analyzing feature importance to optimize model performance and feature engineering

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills model-explainability-tool

For use in Claude.ai and ChatGPT

Download Skill

Key Features

01Identification of primary feature importance and influential model drivers

02Fairness and transparency auditing for machine learning development workflows

03Stakeholder-ready summaries of complex model logic and decision paths

04Debugging of unexpected model predictions and complex feature interactions

05Automated SHAP and LIME explanation generation for various model types

06883 GitHub stars

Use Cases

01Explaining specific model predictions for regulatory or business compliance inquiries

02Identifying and mitigating bias by auditing feature influences on model outcomes

03Analyzing feature importance to optimize model performance and feature engineering

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills model-explainability-tool

For use in Claude.ai and ChatGPT

Download Skill