Learn SHAP, LIME, PDP, and other model-agnostic methods to make machine learning models transparent and understandable.
What you'll learn
- Explain the importance of explainable and interpretable AI in real-world applications.
- Apply model-agnostic interpretation methods such as SHAP and LIME.
- Use Python libraries (SHAP, LIME, PDP, ELI5, Skater, Captum) to interpret machine learning models.
- Evaluate and compare different interpretation methods to understand their strengths and limitations.