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
Tradeoff and Limitations
Practice: Comparing LIME and SHAP Outputs
Quiz for Chapter 4
Challenges in Model Interpretation
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- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Christoph Molnar, 2024 (Self-published (available online)) - Provides a comprehensive overview of model interpretability, covering various methods and explicitly discussing challenges such as the accuracy-interpretability trade-off, faithfulness, and evaluation.
- Why Should I Trust You? Explaining the Predictions of Any Classifier, Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin, 2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM) DOI: 10.1145/2939672.2939778 - Introduces LIME, a local model-agnostic explanation method, which highlights the challenge of local faithfulness for post-hoc explanations.
- A Unified Approach to Interpreting Model Predictions, Scott Lundberg, Su-In Lee, 2017 Advances in Neural Information Processing Systems 30, Vol. 30 DOI: 10.48550/arXiv.1705.07874 - Presents SHAP, a method for explaining model predictions based on Shapley values, addressing issues like computational expense for exact solutions and feature dependencies.