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
Interpreting Explanations for Regression Models
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- "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 (ACM) DOI: 10.1145/2939672.2939778 - Introduces the LIME technique for explaining machine learning model predictions, including its application to regression tasks.
- A Unified Approach to Interpreting Model Predictions, Scott M. Lundberg and Su-In Lee, 2017 Advances in Neural Information Processing Systems (NeurIPS 30), Vol. 30 (Curran Associates, Inc.) DOI: 10.5555/3295222.3295230 - Presents the SHAP framework, which unifies various interpretability methods and provides a theoretically sound approach for feature attribution in models, including regression.
- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Christoph Molnar, 2024 - A comprehensive guide covering a wide range of model interpretability methods, including detailed sections on LIME and SHAP for various machine learning tasks like regression.
- SHAP Documentation, Scott Lundberg and SHAP Contributors, 2024 - The official documentation for the SHAP Python library, providing usage examples, API reference, and details relevant to applying SHAP to regression models.
- LIME: Local Interpretable Model-agnostic Explanations Documentation, Marco Ribeiro, Sameer Singh, Carlos Guestrin, and LIME Contributors, 2024 - The official documentation for the LIME Python library, offering practical guidance and code examples for explaining individual regression predictions.
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