Machine Learning Model Explainability

About

This skill empowers Claude to analyze machine learning models, offering clear insights into why specific predictions are made and identifying the most influential features driving model outputs. By leveraging advanced interpretability techniques like SHAP and LIME, it helps developers and data scientists debug model performance, ensure algorithmic fairness, and communicate complex insights to non-technical stakeholders in a clear, actionable format.

Key Features

• Feature importance ranking and analysis
• Model debugging and interaction identification
• Automated SHAP and LIME value calculation
• Local explanation generation for specific predictions
• 3 GitHub stars
• Stakeholder-friendly insight summaries

Use Cases

• Identifying global feature importance to improve model transparency and fairness
• Explaining individual model predictions for high-stakes decisions like loan approvals
• Debugging unexpected model behavior by analyzing complex feature interactions