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
Interpreting LIME Explanations
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- Why Should I Trust You? Explaining the Predictions of Any Classifier, Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin, 2016 KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM) DOI: 10.1145/2939672.2939778 - Introduces the LIME algorithm, its theoretical foundation, and the method for generating and interpreting local, model-agnostic explanations.
- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Christoph Molnar, 2024 (Christoph Molnar) - Provides a detailed, accessible chapter on LIME, covering its mechanics, how to interpret its output for various data types, and its advantages and limitations.
- Practical Interpretable Machine Learning: Master the Art of XAI, Sergiy Karayev, Gigi Tang, Josh S. Taylor, and Michelle T. Lee, 2020 (O'Reilly Media) - Offers practical insights into applying and interpreting LIME, with examples and considerations for real-world scenarios.
- Anchors: High-Precision Model-Agnostic Explanations, Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin, 2018 AAAI '18: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, Vol. 32 (Association for the Advancement of Artificial Intelligence) DOI: 10.1609/aaai.v32i1.11491 - Discusses limitations of local linearity assumptions in LIME and proposes Anchors as a high-precision alternative, enhancing understanding of LIME's scope and reliability.