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
KernelSHAP: A Model-Agnostic Approach
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- A Unified Approach to Interpreting Model Predictions, Scott M. Lundberg, Su-In Lee, 2017 Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc.) DOI: 10.5555/3295222.3295328 - Original research introducing SHAP, KernelSHAP, and TreeSHAP, unifying model explanation methods based on Shapley values.
- SHAP (SHapley Additive exPlanations) Documentation, Scott Lundberg and SHAP Contributors, 2024 - Provides practical guidance and API reference for implementing SHAP, including KernelSHAP, with code examples.
- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Christoph Molnar, 2024 (Self-published) - Offers a detailed, accessible explanation of KernelSHAP and its context within model interpretability, building on the original paper.
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