Email the Author

You can use this page to email Christoph Molnar about Interpreting Machine Learning Models With SHAP.

Please include an email address so the author can respond to your query

This message will be sent to Christoph Molnar

This site is protected by reCAPTCHA and the Google  Privacy Policy and  Terms of Service apply.

About the Book

Machine learning is transforming fields from healthcare diagnostics to climate change predictions through their predictive performance. However, these complex machine learning models often lack interpretability, which is becoming more essential than ever for debugging, fostering trust, and communicating model insights.

Introducing SHAP, the Swiss army knife of machine learning interpretability:

  • SHAP can be used to explain individual predictions.
  • By combining explanations for individual predictions, SHAP allows to study the overall model behavior.
  • SHAP is model-agnostic – it works with any model, from simple linear regression to deep learning.
  • With its flexibility, SHAP can handle various data formats, whether it’s tabular, image, or text.
  • The Python package shap makes the application of SHAP for model interpretation easy.

This book will be your comprehensive guide to mastering the theory and application of SHAP. It starts with the quite fascinating origins in game theory and explores what splitting taxi costs has to do with explaining machine learning predictions. Starting with using SHAP to explain a simple linear regression model, the book progressively introduces SHAP for more complex models. You’ll learn the ins and outs of the most popular explainable AI method and how to apply it using the shap package.

In a world where interpretability is key, this book is your roadmap to mastering SHAP. For machine learning models that are not only accurate but also interpretable.

Who This Book Is For

This book is for data scientists, statisticians, machine learners, and anyone who wants to learn how to make machine learning models more interpretable. Ideally, you are already familiar with machine learning to get the most out of this book. And you should know your way around Python to follow the code examples.

What's in the Book

Note: Please be aware that the ePub version utilizes MathML for mathematical notations and may not be compatible with all eReaders. Leanpub has a 60-day "100% Happiness Guarantee", so don't hesitate to just try it out. And you'll also get the PDF where the equations look good.

  1. Introduction
  2. A Short History of Shapley Values and SHAP
  3. Theory of Shapley Values
  4. From Shapley Values to SHAP
  5. Estimating SHAP Values
  6. SHAP for Linear Models
  7. Classification with Logistic Regression
  8. SHAP for Additive Models
  9. Understanding Feature Interactions with SHAP
  10. The Correlation Problem
  11. Regressing Using a Random Forest
  12. Image Classification with Partition Explainer
  13. Image Classification with Deep and Gradient Explainer
  14. Explaining Language Models
  15. Limitations of SHAP
  16. Building SHAP Dashboards with Shapash
  17. Alternatives to the shap Library
  18. Extensions of SHAP
  19. Other Applications of Shapley Values in Machine Learning
  20. SHAP Estimators
  21. The Role of Maskers and Background Data

About me (Christoph Molnar)

Author of the free online book Interpretable Machine Learning. I have a background in both statistics and machine learning and did my Ph.D. in interpretable machine learning. After a mix of data scientist jobs and academia, I'm now a full-time machine learning book author.


About the Author

Christoph Molnar’s avatar Christoph Molnar

@ChristophMolnar

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

Logo white 96 67 2x

Publish Early, Publish Often

  • Path
  • There are many paths, but the one you're on right now on Leanpub is:
  • Shap › Email Author › New
    • READERS
    • Newsletters
    • Weekly Sale
    • Monthly Sale
    • Store
    • Home
    • Redeem a Token
    • Search
    • Support
    • Leanpub FAQ
    • Leanpub Author FAQ
    • Search our Help Center
    • How to Contact Us
    • FRONTMATTER PODCAST
    • Featured Episode
    • Episode List
    • MEMBERSHIPS
    • Reader Memberships
    • Department Reader Memberships
    • Author Memberships
    • Your Membership
    • COMPANY
    • About
    • About Leanpub
    • Blog
    • Contact
    • Press
    • Essays
    • AI Services
    • Imagine a world...
    • Manifesto
    • More
    • Partner Program
    • Causes
    • Accessibility
    • AUTHORS
    • Write and Publish on Leanpub
    • Create a Book
    • Create a Bundle
    • Create a Course
    • Create a Track
    • Testimonials
    • Why Leanpub
    • Services
    • TranslateAI
    • TranslateWord
    • TranslateEPUB
    • PublishWord
    • Publish on Amazon
    • CourseAI
    • GlobalAuthor
    • Marketing Packages
    • IndexAI
    • Author Newsletter
    • The Leanpub Author Update
    • Author Support
    • Author Help Center
    • Leanpub Authors Forum
    • The Leanpub Manual
    • Supported Languages
    • The LFM Manual
    • Markua Manual
    • API Docs
    • Organizations
    • Learn More
    • Sign Up
    • LEGAL
    • Terms of Service
    • Copyright Policy
    • Privacy Policy
    • Refund Policy

*   *   *

Leanpub is copyright © 2010-2025 Ruboss Technology Corp.
All rights reserved.

This site is protected by reCAPTCHA
and the Google  Privacy Policy and  Terms of Service apply.

Leanpub requires cookies in order to provide you the best experience. Dismiss