Interpretable Machine Learning
Interpretable Machine Learning
A Guide for Making Black Box Models Explainable
About the Book
A free HTML version of the book can be found at: https://christophm.github.io/interpretable-ml-book/
The print version can be bought here: http://bit.ly/iml-paperback
"Thank you @ChristophMolnar for the great work on #MachineLearning Interpretability!" - @HJDLopes
"If you are looking for a good introduction to interpretable/explainable machine learning, this book is great. It covers lots of ground quickly and is well written, and is very up-to-date." - Tim Miller
"New book on interpretable #AI by @ChristophMolnar very much needed!" - @AjitJaokar
Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable.
After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME.
All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.
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About the Author
About the Contributors
Cover designer
Table of Contents
- Preface
-
Introduction
- Story Time
- What Is Machine Learning?
- Terminology
-
Interpretability
- Importance of Interpretability
- Taxonomy of Interpretability Methods
- Scope of Interpretability
- Evaluation of Interpretability
- Properties of Explanations
- Human-friendly Explanations
-
Datasets
- Bike Rentals (Regression)
- YouTube Spam Comments (Text Classification)
- Risk Factors for Cervical Cancer (Classification)
-
Interpretable Models
- Linear Regression
- Logistic Regression
- GLM, GAM and more
- Decision Tree
- Decision Rules
- RuleFit
- Other Interpretable Models
-
Model-Agnostic Methods
- Partial Dependence Plot (PDP)
- Individual Conditional Expectation (ICE)
- Accumulated Local Effects (ALE) Plot
- Feature Interaction
- Feature Importance
- Global Surrogate
- Local Surrogate (LIME)
- Shapley Values
-
Example-Based Explanations
- Counterfactual Explanations
- Adversarial Examples
- Prototypes and Criticisms
- Influential Instances
-
A Look into the Crystal Ball
- The Future of Machine Learning
- The Future of Interpretability
- Contribute to the Book
- Citing this Book
- Acknowledgements
-
References
- R Packages Used for Examples
- Notes
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