Interpreting Machine Learning Models With SHAP

A Guide With Python Examples And Theory On Shapley Values

Interpreting Machine Learning Models With SHAP cover image

Master machine learning interpretability with this comprehensive guide to SHAP – your tool to communicating model insights and building trust in all your machine learning applications.

Where To Buy

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 origin 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

  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

Improve Your Modeling With Interpretability!