Module 21: Model Explainability

Learn to explain and interpret machine learning models.

What You'll Learn

• Feature Importance Methods
• SHAP (SHapley Additive exPlanations)
• LIME (Local Interpretable Model-agnostic Explanations)
• Partial Dependence Plots
• Best Practices for Explainability

Topics Covered

1. Introduction to Explainability

• Why explainability matters
• Types of explanations
• Global vs Local explanations

2. Feature Importance

• Tree-based importance
• Permutation importance
• Model-agnostic methods

3. SHAP

• Tree SHAP
• Kernel SHAP
• SHAP visualizations

4. LIME

• Local explanations
• Tabular data
• Text and image data

5. Partial Dependence Plots

• Understanding feature effects
• Interaction effects

Learning Objectives

By the end of this module, you should be able to:

• Explain model predictions
• Understand feature contributions
• Use SHAP and LIME effectively
• Create interpretable models
• Communicate findings to stakeholders

Projects

1. Explain Credit Scoring: Understand loan approval decisions
2. Medical Diagnosis: Explain disease prediction
3. Fraud Detection: Understand fraud indicators

Key Concepts

• SHAP Values: Unified measure of feature importance
• LIME: Local linear approximations
• Feature Importance: Global model understanding
• Partial Dependence: Feature effect visualization

Documentation & Learning Resources

Official Documentation:

• SHAP Documentation
• LIME Documentation

Free Courses:

• Interpretable Machine Learning Book

Complete Detailed Guide →

Additional Resources:

• Advanced Topics →: Advanced SHAP, LIME for different data types
• Project Tutorial →: Step-by-step credit scoring explanation
• Quick Reference →: Quick lookup guide

Previous Module: 20-handling-imbalanced-data
Next Module: Continue with projects or advanced topics