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
Intuition Behind LIME
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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 original paper introducing LIME, outlining its methodology for explaining individual predictions of black-box models.
- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Christoph Molnar, 2024 (Lulu.com) - A comprehensive book providing an accessible explanation of LIME, including its mechanics and application within the field of interpretable machine learning.
- Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges Toward Responsible AI, Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Jon Ander Del Ser, Anunziata Bennetot, Serguei Tabik, Alberto Benjamins, Rogelio Herrera, F. Javier Cabrerizo, Maite Durán, F. Herrera, 2020 Information Fusion, Vol. 58 (Elsevier) DOI: 10.1016/j.inffus.2019.05.004 - This review paper positions LIME within the broader context of Explainable AI (XAI), discussing its role and characteristics alongside other explanation methods.