Section 2 – Practical Problem Solving

This section will give you the information and experience needed to implement the various approaches to model explainability using Python. You will learn about the different Python frameworks for implementing the concepts of Explainable AI (XAI) covered in the previous section, such as LIME, SHAP, TCAV, ALIBI, DALEX, Explainerdashboard, InterpretML, ELI5, and DiCE. You will also get the necessary practical exposure to apply explainability methods for practical use cases.

This section comprises the following chapters:

• Chapter 4, LIME for Model Interpretability
• Chapter 5, Practical Exposure to Using LIME in ML
• Chapter 6, Model Interpretability Using SHAP
• Chapter 7, Practical Exposure to Using SHAP in ML
• Chapter 8, Human-Friendly Explanations with TCAV
• Chapter 9, Other Popular XAI Frameworks