Interpretable Machine Learning (Second Edition)
Minimum price
Suggested price

Interpretable Machine Learning (Second Edition)

A Guide for Making Black Box Models Explainable

About the Book

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. The focus of the book is on model-agnostic methods for interpreting black box models such as feature importance and accumulated local effects, and explaining individual predictions with Shapley values and LIME. In addition, the book presents methods specific to deep neural networks.

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. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.


"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

About The 2nd Edition

The 2nd edition of Interpretable Machine Learning offers a substantial improvement over the 1st edition. The book now also covers approaches specific to interpreting deep neural networks. The update also brings many new model-agnostic interpretation methods such as the popular SHAP, Anchors and functional decomposition. The 2nd edition also improves the arrangement of the chapters and fixes smaller typos and errors.

The Updates in Detail

All the methods chapters are now organized into four main chapters: interpretable models, global methods, local methods and deep learning specific methods. 

The following new chapters were added:

  • Functional Decomposition
  • Scoped Rules (Anchors)
  • SHAP (SHapley Additive exPlanations)
  • Preface by The Author

A new section about Neural Network Interpretation was added, containing the following (new) chapters:

  • Learned Features
  • Pixel Attribution (Saliency Maps)
  • Detecting Concepts
  • Adversarial Examples (already in 1st edition, but moved to this chapter)
  • Influential Instances (already in 1st edition, but also moved to this chapter)

The following chapters got updated:

  • Counterfactual explanations: The chapter was improved overall and Multi-objective counterfactual explanations were introduced.
  • Partial Dependence Plots: A paragraph on PDP-based feature importance was added.
  • Permutation Feature Importance:  The chapter was renamed, now lists alternative approaches, and a clear recommendation to use test data was added.
  • Accumulated Local Effect Plots: Some additional disadvantages of the approach were added.
  • Shapley Values: The explanation for conditional sampling  for Shapley values was improved.

Also, a few things were fixed:

  • A mixup of the coding of the seasons in the bike rental data was fixed.
  • Also the coding for cervical cancer outcome was fixed. This mostly impacts the logistic regression and the decision rules chapter.
  • An error in the formula for R-squared was fixed.
  • Many smaller typos and problems were. Here also a big thanks to all the readers who submitted fixes on Github!
  • A lot of new software packages for interpretable machine learning are now available, and the 2nd edition now also lists newer candidates.

Other Versions

The print version can be bought on Amazon.

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.

Christoph Molnar

Episode 120

About the Contributors


Cover designer

Table of Contents

  • Summary
  • 1 Preface by the Author
  • 2 Introduction
    • 2.1 Story Time
      • Lightning Never Strikes Twice
      • Trust Fall
      • Fermi’s Paperclip
    • 2.2 What Is Machine Learning?
    • 2.3 Terminology
  • 3 Interpretability
    • 3.1 Importance of Interpretability
    • 3.2 Taxonomy of Interpretability Methods
    • 3.3 Scope of Interpretability
  • 4 Datasets
    • 4.1 Bike Rentals (Regression)
    • 4.2 YouTube Spam Comments (Text Classification)
    • 4.3 Risk Factors for Cervical Cancer (Classification)
  • 5 Interpretable Models
    • 5.1 Linear Regression
      • 5.2 Logistic Regression
      • 5.3 GLM, GAM and more
      • 5.4 Decision Tree
      • 5.5 Decision Rules
      • 5.6 RuleFit
      • 5.7 Other Interpretable Models
    • 6 Model-Agnostic Methods
    • 7 Example-Based Explanations
    • 8 Global Model-Agnostic Methods
      • 8.1 Partial Dependence Plot (PDP)
      • 8.2 Accumulated Local Effects (ALE) Plot
      • 8.3.1 Feature Interaction
      • 8.4 Functional Decompositon
      • 8.5 Permutation Feature Importance
      • 8.6 Global Surrogate
      • 8.7 Prototypes and Criticisms
    • 9 Local Model-Agnostic Methods
      • 9.1 Individual Conditional Expectation (ICE)
      • 9.2 Local Surrogate (LIME)
      • 9.3 Counterfactual Explanations
      • 9.4 Scoped Rules (Anchors)
      • 9.5 Shapley Values
      • 9.6 SHAP (SHapley Additive exPlanations)
    • 10 Neural Network Interpretation
      • 10.1 Learned Features
      • 10.2 Pixel Attribution (Saliency Maps)
      • 10.3 Detecting Concepts
      • 10.4 Adversarial Examples
      • 10.5 Influential Instances
    • 11 A Look into the Crystal Ball
    • 12 Contribute to the Book
    • 13 Citing this Book
    • 14 Translations
    • 15 Acknowledgements
    • 16 References

The Leanpub 60-day 100% Happiness Guarantee

Within 60 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.

See full terms

Do Well. Do Good.

Authors have earned$11,820,291writing, publishing and selling on Leanpub, earning 80% royalties while saving up to 25 million pounds of CO2 and up to 46,000 trees.

Learn more about writing on Leanpub

Free Updates. DRM Free.

If you buy a Leanpub book, you get free updates for as long as the author updates the book! Many authors use Leanpub to publish their books in-progress, while they are writing them. All readers get free updates, regardless of when they bought the book or how much they paid (including free).

Most Leanpub books are available in PDF (for computers) and EPUB (for phones, tablets and Kindle). The formats that a book includes are shown at the top right corner of this page.

Finally, Leanpub books don't have any DRM copy-protection nonsense, so you can easily read them on any supported device.

Learn more about Leanpub's ebook formats and where to read them

Write and Publish on Leanpub

You can use Leanpub to easily write, publish and sell in-progress and completed ebooks and online courses!

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. (Or, if you are producing your ebook your own way, you can even upload your own PDF and/or EPUB files and then publish with one click!) It really is that easy.

Learn more about writing on Leanpub