Learn how to understand the prediction rationale of machine learning models and build trustworthy AI systems using interpretation techniques such as SHAP, LIME, and Grad-CAM
Series Overview
This series is a practical educational content consisting of four chapters that teaches the theory and implementation of model interpretability and explainability in machine learning progressively from fundamentals.
Model Interpretability is a technology that explains the prediction rationale of machine learning models, which tend to become black boxes, in a human-understandable form. Techniques such as SHAP (Shapley value-based feature importance), LIME (local linear approximation), and Grad-CAM (convolutional neural network visualization) enable quantitative explanation of "why this prediction was made." It has become an essential technology in fields requiring accountability such as medical diagnosis, financial assessment, and autonomous driving, and the "right to explanation" is explicitly stated in regulations such as the EU General Data Protection Regulation (GDPR). You will understand and be able to implement cutting-edge technologies being researched and put into practical use by companies such as Google, Microsoft, and IBM. We provide practical knowledge using major libraries such as SHAP, LIME, ELI5, and Captum.
Features:
- ✅ From Theory to Practice: Systematic learning from interpretability concepts to implementation and visualization
- ✅ Implementation-Focused: Over 30 executable Python/SHAP/LIME/Captum code examples
- ✅ Business-Oriented: Practical interpretation methods assuming real business challenges
- ✅ Latest Technology Compliance: Implementation using SHAP, LIME, Grad-CAM, and Integrated Gradients
- ✅ Practical Applications: Interpretation of tabular data, image, and text models
Total Study Time: 4-5 hours (including code execution and exercises)
How to Progress Through Learning
Recommended Learning Order
For Beginners (completely new to model interpretability):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 (all chapters recommended)
- Duration: 4-5 hours
For Intermediate Learners (experienced in ML development):
- Chapter 1 (overview) → Chapter 2 → Chapter 3 → Chapter 4
- Duration: 3-4 hours
For Reinforcing Specific Topics:
- Interpretability basics, global/local interpretation: Chapter 1 (focused learning)
- SHAP, Shapley values: Chapter 2 (focused learning)
- LIME, Permutation Importance: Chapter 3 (focused learning)
- Grad-CAM, Attention visualization: Chapter 4 (focused learning)
- Duration: 50-70 min/chapter
Chapter Details
Chapter 1: Basics of Model Interpretability
Difficulty: Intermediate
Reading Time: 50-60 min
Code Examples: 5
Learning Content
- Why Interpretability Matters - Trustworthiness, fairness, debugging, regulatory compliance
- Interpretability vs Explainability - Interpretability vs Explainability
- Global Interpretation vs Local Interpretation - Entire model vs individual predictions
- Classification of Interpretation Methods - Model-specific methods vs model-agnostic methods
- Trade-off Between Interpretability and Accuracy - Linear models vs black-box models
Learning Objectives
- ✅ Able to explain the importance of model interpretability
- ✅ Able to distinguish between global and local interpretation
- ✅ Able to understand the classification of interpretation methods
- ✅ Able to explain the trade-off between interpretability and accuracy
- ✅ Able to select appropriate interpretation methods
Chapter 2: SHAP (SHapley Additive exPlanations)
Difficulty: Intermediate
Reading Time: 60-70 min
Code Examples: 10
Learning Content
- Shapley Value Theory - Derivation from game theory, axiomatic properties
- Basic Concepts of SHAP - Additivity, local accuracy, consistency
- TreeSHAP - Fast interpretation of decision trees, random forests, and XGBoost
- DeepSHAP - Interpretation of neural networks
- SHAP Visualization - Waterfall, Force, Summary, and Dependence plots
Learning Objectives
- ✅ Able to understand the theoretical background of Shapley values
- ✅ Able to calculate feature importance with SHAP
- ✅ Able to interpret tree-based models with TreeSHAP
- ✅ Able to interpret neural networks with DeepSHAP
- ✅ Able to explain prediction rationale with SHAP visualization
Chapter 3: LIME & Other Methods
Difficulty: Intermediate
Reading Time: 60-70 min
Code Examples: 9
Learning Content
- LIME (Local Interpretable Model-agnostic Explanations) - Local linear approximation, sampling-based interpretation
- Permutation Importance - Importance calculation by feature shuffling
- PDP (Partial Dependence Plot) - Visualization of relationship between features and predictions
- ICE (Individual Conditional Expectation) - Conditional expectation values for individual samples
- Anchors - Rule-based local interpretation
Learning Objectives
- ✅ Able to explain local prediction rationale with LIME
- ✅ Able to calculate feature importance with Permutation Importance
- ✅ Able to visualize relationship between features and predictions with PDP
- ✅ Able to understand behavior of individual samples with ICE
- ✅ Able to judge application scenarios for each method
Chapter 4: Deep Learning Interpretation
Difficulty: Intermediate
Reading Time: 60-70 min
Code Examples: 8
Learning Content
- Grad-CAM (Gradient-weighted Class Activation Mapping) - Visualization of CNN attention regions
- Integrated Gradients - Gradient-based feature importance
- Attention Visualization - Interpretation of Transformer attention mechanisms
- Saliency Maps - Visualization of gradients with respect to input
- Layer-wise Relevance Propagation (LRP) - Importance calculation by backpropagation
Learning Objectives
- ✅ Able to visualize CNN attention regions with Grad-CAM
- ✅ Able to calculate feature importance with Integrated Gradients
- ✅ Able to interpret Transformers with Attention visualization
- ✅ Able to understand input influence with Saliency Maps
- ✅ Able to judge characteristics and application scenarios for each method
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Able to explain the importance of model interpretability and regulatory requirements
- ✅ Understanding the difference between global and local interpretation
- ✅ Able to explain the theoretical background of SHAP, LIME, and Grad-CAM
- ✅ Understanding the characteristics and application scenarios of each interpretation method
- ✅ Able to explain the trade-off between interpretability and accuracy
Practical Skills (Doing)
- ✅ Able to calculate and visualize feature importance with SHAP
- ✅ Able to explain local prediction rationale with LIME
- ✅ Able to visualize CNN attention regions with Grad-CAM
- ✅ Able to analyze features with Permutation Importance and PDP
- ✅ Able to interpret deep learning models with Integrated Gradients and Attention
Application Ability (Applying)
- ✅ Able to select appropriate interpretation methods for problems
- ✅ Able to explain model prediction rationale to stakeholders
- ✅ Able to evaluate and improve models from interpretability perspective
- ✅ Able to design explainable AI systems
- ✅ Able to create interpretation reports compliant with regulatory requirements
Prerequisites
To effectively learn this series, it is desirable to have the following knowledge:
Required (Must Have)
- ✅ Python Basics: Variables, functions, classes, NumPy, pandas
- ✅ Machine Learning Basics: Concepts of training, evaluation, and features
- ✅ scikit-learn Basics: Model training, prediction, evaluation
- ✅ Statistics Basics: Mean, variance, correlation, distribution
- ✅ Linear Algebra Basics: Vectors, matrices (recommended)
Recommended (Nice to Have)
- 💡 Deep Learning Basics: PyTorch/TensorFlow (for Chapter 4)
- 💡 Game Theory Basics: For understanding Shapley value theory
- 💡 Visualization Libraries: matplotlib, seaborn
- 💡 Tree-based Models: XGBoost, LightGBM (for Chapter 2)
- 💡 CNN Basics: Convolution, pooling (for Chapter 4)
Recommended Prior Learning:
- 📚 - ML fundamental knowledge