Intro
Foreword
Preface
Why This Book?
Who This Book Is For
What This Book Covers
Acknowledgments
Contents
Notation
Calculus
Datasets
Functions
Variables
Probability
Sets
1 Introduction to Interpretability and Explainability
1.1 Black-Box problem
1.2 Goals
1.3 Brief History
1.3.1 Porphyrian Tree
1.3.2 Expert Systems
1.3.3 Case-Based Reasoning
1.3.4 Bayesian Networks
1.3.5 Neural Networks
1.4 Purpose
1.5 Societal Impact
1.6 Types of Explanations
1.7 Trade-offs
1.8 Taxonomy
1.8.1 Scope
1.8.2 Stage

1.9 Flowchart for Interpretable and Explainable Techniques
1.10 Resources for Researchers and Practitioners
1.10.1 Books
1.10.2 Relevant University Courses and Classes
1.10.3 Online Resources
1.10.4 Survey Papers
1.11 Book Layout and Details
1.11.1 Structure: Explainable Algorithm
1.11.1.1 Linear Regression
References
2 Pre-model Interpretability and Explainability
2.1 Data Science Process and EDA
2.2 Exploratory Data Analysis
2.2.1 EDA Challenges for Explainability
2.2.2 EDA: Taxonomy
2.2.3 Role of EDA in Explainability

2.2.4 Non-graphical: Summary Statistics and Analysis
2.2.4.1 Tools and Libraries
2.2.4.2 Summary Statistics and Analysis
2.2.5 Graphical: Univariate and Multivariate Analysis
2.2.5.1 Tools and Libraries
2.2.5.2 Univariate Analysis
2.2.5.3 Multivariate Analysis
2.2.6 EDA and Time Series
2.2.6.1 Resampling
2.2.6.2 Seasonality and Trend Analysis
2.2.6.3 Autocorrelation, Stationarity, and Differencing
2.2.7 EDA and NLP
2.2.7.1 Text Corpus Statistics
2.2.7.2 N-Grams Analysis
2.2.7.3 Word Cloud
2.2.7.4 Topic Modeling
2.2.7.5 Corpus Visualization

2.2.8 EDA and Computer Vision
2.2.8.1 Distributional Analysis
2.2.8.2 2D Projections
2.3 Feature Engineering
2.3.1 Feature Engineering and Explainability
2.3.2 Feature Engineering Taxonomy and Tools
2.3.2.1 Filter-Based
2.3.2.2 Wrapper-Based
2.3.2.3 Unsupervised
2.3.2.4 Embedded
References
3 Model Visualization Techniques and Traditional Interpretable Algorithms
3.1 Model Validation, Evaluation, and Hyperparameters
3.1.1 Tools and Libraries
3.2 Model Selection and Visualization
3.2.1 Validation Curve
3.2.2 Learning Curve

3.3 Classification Model Visualization
3.3.1 Confusion Matrix and Classification Report
3.3.2 ROC and AUC
3.3.3 PRC
3.3.4 Discrimination Thresholds
3.4 Regression Model Visualization
3.4.1 Residual Plots
3.4.2 Prediction Error Plots
3.4.3 Alpha Selection Plots
3.4.4 Cook's Distance
3.5 Clustering Model Visualization
3.5.1 Elbow Method
3.5.2 Silhouette Coefficient Visualizer
3.5.3 Intercluster Distance Maps
3.6 Interpretable Machine Learning Properties
3.7 Traditional Interpretable Algorithms
3.7.1 Tools and Libraries
3.7.2 Linear Regression