Model Interpretability with SHAP and LIME
Chapter 1: Foundations of Model Interpretability
Why Explain Model Predictions?
Interpretability vs. Explainability
Taxonomy of Interpretability Methods
Scope of Explanations: Global vs. Local
Challenges in Model Interpretation
Quiz for Chapter 1
Chapter 2: Local Interpretable Model-agnostic Explanations (LIME)
Intuition Behind LIME
How LIME Works: Perturbation and Surrogate Models
Applying LIME to Tabular Data
Applying LIME to Text Data
Interpreting LIME Explanations
LIME Implementation with Python
Limitations and Considerations for LIME
Hands-on Practical: Generating LIME Explanations
Quiz for Chapter 2
Chapter 3: SHapley Additive exPlanations (SHAP)
Introduction to Shapley Values
SHAP Values: Connecting Shapley to Model Features
Properties of SHAP Values
KernelSHAP: A Model-Agnostic Approach
TreeSHAP: Optimized for Tree-Based Models
Interpreting SHAP Plots: Force Plots
Interpreting SHAP Plots: Summary and Dependence Plots
SHAP Implementation with Python
Hands-on Practical: Calculating SHAP Values
Quiz for Chapter 3
Chapter 4: Comparing and Applying Interpretability Techniques
LIME vs. SHAP: Differences
LIME vs. SHAP: Strengths and Weaknesses
Choosing Between LIME and SHAP
Interpreting Explanations for Regression Models
Interpreting Explanations for Classification Models
Integrating Interpretability into the ML Workflow
Common Gotchas and Considerations
Practice: Comparing LIME and SHAP Outputs
Quiz for Chapter 4
Limitations and Considerations for LIME
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- "Why Should I Trust You?": Explaining the Predictions of Any Classifier, Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin, 2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16) (Association for Computing Machinery) DOI: 10.1145/2939672.2939778 - Introduces LIME, outlining its methodology for local model interpretability, and is the foundational paper for this technique.
- On the Stability of Feature Importance Explanations, David Alvarez-Melis, Tommi S. Jaakkola, 2018 Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32(1) (AAAI Press) DOI: 10.1609/aaai.v32i1.12177 - Examines the stability and robustness of feature importance explanations from methods like LIME, highlighting their sensitivity to small perturbations.
- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Christoph Molnar, 2023 - Provides a thorough overview of LIME's operation and discusses its constraints, such as faithfulness, local neighborhood definition, and surrogate model selections.