Interpretable Machine Learning
Interpretable Machine Learning
Minimum price
Suggested price
Interpretable Machine Learning

This book is 98% complete

Last updated on 2019-01-22

About the Book

"Thank you @ChristophMolnar for the great work on #MachineLearning Interpretability!" - @HJDLopes

"Book on interpretability of ML models, such an important topic often neglected" - @prdeepakbabu

"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 don’t 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. The later chapters focus on general model-agnostic tools for interpreting black box models and explaining individual predictions. In an ideal future, machines will be able to explain their decisions and the algorithmic age we move toward will be as human as possible.

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. This book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning more interpretable.

A free HTML version of the book can be found at:

About the Author

Christoph Molnar
Christoph Molnar

On a mission to make algorithms more interpretable by combining machine learning and statistics.

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
  • Notes

The Leanpub 45-day 100% Happiness Guarantee

Within 45 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.

See full terms...

Write and Publish on Leanpub

Authors, publishers and universities use Leanpub to publish amazing in-progress and completed books and courses, just like this one. You can use Leanpub to write, publish and sell your book or course as well! Leanpub is a powerful platform for serious authors, combining a simple, elegant writing and publishing workflow with a store focused on selling in-progress ebooks. Leanpub is a magical typewriter for authors: just write in plain text, and to publish your ebook, just click a button. It really is that easy.

Learn more about writing on Leanpub