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
Taxonomy of Interpretability Methods
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- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Christoph Molnar, 2023 - A comprehensive online book offering a structured overview of machine learning interpretability methods, including various classification dimensions, local/global explanations, and model-specific/agnostic techniques.
- "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 (Association for Computing Machinery) DOI: 10.1145/2939672.2939778 - The original paper introducing LIME, a widely adopted post-hoc and model-agnostic method that generates local, interpretable explanations for individual predictions of any classifier.
- A Unified Approach to Interpreting Model Predictions, Scott M Lundberg, Su-In Lee, 2017 Advances in Neural Information Processing Systems 30 (NIPS 2017) (NeurIPS) - The seminal paper presenting SHAP, a game-theoretic explanation framework that unifies various interpretability methods and provides consistent feature attributions, including model-agnostic (KernelSHAP) and model-specific (TreeSHAP) implementations.
- A Survey of Explainable Artificial Intelligence (XAI): Methods, Applications, Challenges, and Opportunities, Amina Adadi, Mohammed Berrado, 2018 Applied System Innovation, Vol. 1 (MDPI) DOI: 10.3390/asi1040031 - A comprehensive survey that categorizes Explainable AI (XAI) methods based on several dimensions, including the distinction between model-agnostic/specific and pre-model/post-model approaches, offering a broad overview of the field.