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
Interpreting Explanations for Classification Models
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- "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 (KDD '16) (ACM) DOI: 10.1145/2939672.2939778 - The foundational paper introducing LIME, outlining its theoretical basis and algorithm for local, model-agnostic explanations.
- A Unified Approach to Interpreting Model Predictions, Scott M. Lundberg and Su-In Lee, 2017 Advances in Neural Information Processing Systems 30 (NIPS 2017), Vol. 30 (Curran Associates, Inc.) - The original paper that introduced SHAP values, unifying various explanation methods and grounding them in Shapley values from game theory.
- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Christoph Molnar, 2024 (Self-published) - A comprehensive online book covering various methods for interpretable machine learning, including detailed sections on LIME and SHAP.
- SHAP Documentation, Scott Lundberg and SHAP contributors, 2024 - Official documentation for the SHAP Python library, offering practical implementation details, examples, and descriptions for various visualization types.