Interpretable machine learning
Preface
1
Introduction
1.1
Story Time
Lightning Never Strikes Twice
Trust Fall
Fermi’s Paperclips
1.2
What Is Machine Learning?
1.3
Terminology
2
Interpretability
2.1
Importance of Interpretability
2.2
Taxonomy of Interpretability Methods
2.3
Scope of Interpretability
2.3.1
Algorithm Transparency
2.3.2
Global, Holistic Model Interpretability
2.3.3
Global Model Interpretability on a Modular Level
2.3.4
Local Interpretability for a Single Prediction
2.3.5
Local Interpretability for a Group of Predictions
2.4
Evaluation of Interpretability
2.5
Properties of Explanations
2.6
Human-friendly Explanations
2.6.1
What Is an Explanation?
2.6.2
What Is a Good Explanation?
3
Datasets
3.1
Bike Rentals (Regression)
3.2
YouTube Spam Comments (Text Classification)
3.3
Risk Factors for Cervical Cancer (Classification)
4
Interpretable Models
4.1
Linear Regression
4.1.1
Interpretation
4.1.2
Example
4.1.3
Visual Interpretation
4.1.4
Explain Individual Predictions
4.1.5
Encoding of Categorical Features
4.1.6
Do Linear Models Create Good Explanations?
4.1.7
Sparse Linear Models
4.1.8
Advantages
4.1.9
Disadvantages
4.2
Logistic Regression
4.2.1
What is Wrong with Linear Regression for Classification?
4.2.2
Theory
4.2.3
Interpretation
4.2.4
Example
4.2.5
Advantages and Disadvantages
4.2.6
Software
4.3
GLM, GAM and more
4.3.1
Non-Gaussian Outcomes - GLMs
4.3.2
Interactions
4.3.3
Nonlinear Effects - GAMs
4.3.4
Advantages
4.3.5
Disadvantages
4.3.6
Software
4.3.7
Further Extensions
4.4
Decision Tree
4.4.1
Interpretation
4.4.2
Example
4.4.3
Advantages
4.4.4
Disadvantages
4.4.5
Software
4.5
Decision Rules
4.5.1
Learn Rules from a Single Feature (OneR)
4.5.2
Sequential Covering
4.5.3
Bayesian Rule Lists
4.5.4
Advantages
4.5.5
Disadvantages
4.5.6
Software and Alternatives
4.6
RuleFit
4.6.1
Interpretation and Example
4.6.2
Theory
4.6.3
Advantages
4.6.4
Disadvantages
4.6.5
Software and Alternative
4.7
Other Interpretable Models
4.7.1
Naive Bayes Classifier
4.7.2
K-Nearest Neighbors
5
Model-Agnostic Methods
5.1
Partial Dependence Plot (PDP)
5.1.1
Examples
5.1.2
Advantages
5.1.3
Disadvantages
5.1.4
Software and Alternatives
5.2
Individual Conditional Expectation (ICE)
5.2.1
Examples
5.2.2
Advantages
5.2.3
Disadvantages
5.2.4
Software and Alternatives
5.3
Accumulated Local Effects (ALE) Plot
5.3.1
Motivation and Intuition
5.3.2
Theory
5.3.3
Estimation
5.3.4
Examples
5.3.5
Advantages
5.3.6
Disadvantages
5.3.7
Implementation and Alternatives
5.4
Feature Interaction
5.4.1
Feature Interaction?
5.4.2
Theory: Friedman’s H-statistic
5.4.3
Examples
5.4.4
Advantages
5.4.5
Disadvantages
5.4.6
Implementations
5.4.7
Alternatives
5.5
Feature Importance
5.5.1
Theory
5.5.2
Should I Compute Importance on Training or Test Data?
5.5.3
Example and Interpretation
5.5.4
Advantages
5.5.5
Disadvantages
5.5.6
Software and Alternatives
5.6
Global Surrogate
5.6.1
Theory
5.6.2
Example
5.6.3
Advantages
5.6.4
Disadvantages
5.6.5
Software
5.7
Local Surrogate (LIME)
5.7.1
LIME for Tabular Data
5.7.2
LIME for Text
5.7.3
LIME for Images
5.7.4
Advantages
5.7.5
Disadvantages
5.8
Shapley Values
5.8.1
General Idea
5.8.2
Examples and Interpretation
5.8.3
The Shapley Value in Detail
5.8.4
Advantages
5.8.5
Disadvantages
5.8.6
Software and Alternatives
6
Example-Based Explanations
6.1
Counterfactual Explanations
6.1.1
Generating Counterfactual Explanations
6.1.2
Examples
6.1.3
Advantages
6.1.4
Disadvantages
6.1.5
Software and Alternatives
6.2
Adversarial Examples
6.2.1
Methods and Examples
6.2.2
The Cybersecurity Perspective
6.3
Prototypes and Criticisms
6.3.1
Theory
6.3.2
Examples
6.3.3
Advantages
6.3.4
Disadvantages
6.3.5
Code and Alternatives
6.4
Influential Instances
6.4.1
Deletion Diagnostics
6.4.2
Influence Functions
6.4.3
Advantages of Identifying Influential Instances
6.4.4
Disadvantages of Identifying Influential Instances
6.4.5
Software and Alternatives
7
A Look into the Crystal Ball
7.1
The Future of Machine Learning
7.2
The Future of Interpretability
8
Contribute to the Book
9
Citing this Book
10
Acknowledgements
References
Published with bookdown
Interpretable Machine Learning
References