Comparing and Applying Interpretability Techniques
You have learned the mechanics of LIME and SHAP individually. This chapter brings these two techniques together, providing a comparative perspective to help you choose and apply them effectively.
We will examine the core differences in how LIME and SHAP generate explanations, discussing the relative strengths and weaknesses that arise from these differences. You'll gain practical guidance on selecting the most suitable technique based on your model type, data characteristics, and specific interpretability goals.
Furthermore, we'll look at applying these methods in common machine learning scenarios, specifically for regression and classification tasks, and demonstrate how to integrate explanation generation into your development workflow. Finally, we address common considerations and potential difficulties you might encounter when using LIME and SHAP in practice.
Sections
4.1 LIME vs. SHAP: Conceptual Differences
4.2 LIME vs. SHAP: Strengths and Weaknesses
4.3 Choosing Between LIME and SHAP
4.4 Interpreting Explanations for Regression Models
4.5 Interpreting Explanations for Classification Models
4.6 Integrating Interpretability into the ML Workflow
4.7 Common Gotchas and Considerations
4.8 Practice: Comparing LIME and SHAP Outputs
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