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
Why Explain Model Predictions?
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- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Christoph Molnar, 2024 - Explains motivations and techniques for model interpretability, addressing trust, accountability, debugging, bias detection, and regulatory compliance.
- "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) DOI: 10.48550/arXiv.1602.04938 - A foundational paper that discusses the need for local model explanations to build user confidence and understand individual predictions.
- A Unified Approach to Interpreting Model Predictions, Scott M. Lundberg, Su-In Lee, 2017 Advances in Neural Information Processing Systems (NeurIPS), Vol. 30 DOI: 10.48550/arXiv.1705.07874 - Introduces SHAP values as a unified framework for interpreting predictions, which helps address issues like fairness, debugging, and user confidence.
- Fairness and Machine Learning: Limitations and Opportunities, Solon Barocas, Moritz Hardt, and Arvind Narayanan, 2023 (MIT Press) - Provides a comprehensive overview of fairness in machine learning, covering how models can learn biases and the importance of interpretability for auditing and mitigating them.