In less than 100 pages, Modeling Mindsets elucidates the worldviews behind various statistical modeling and machine learning mindsets.

About The Book

Books on modeling often jump right into math and methods. Drowned in detail, it can take years to appreciate the assumptions and limitations of the various modeling mindsets. Written in a clear and concise style, Modeling Mindsets introduces approaches such as Bayesian inference, supervised learning, causal inference, and more.

After reading this book, you will have a much better understanding of the different approaches to modeling and be able to choose the right one for your problem.

Who This Book Is For

This book is for everyone who builds models from data: data scientists, statisticians, machine learners, and quantitative researchers.

To get the most out of this book:

• You should already have experience with modeling and working with data.
• You should feel comfortable with at least one of the mindsets in this book.

Don't read this book if:

• You are completely new to working with data and models.
• You cling to the mindset you already know and aren't open to other mindsets.

You will get the most out of Modeling Mindsets if you keep an open mind You have to challenge the rigid assumptions of the mindset that feels natural to you.

"It has taken me many years of fumbling around with ML and statistics to achieve a fraction of the intuition in the book. Save yourself the time!"

– Robert Martin

Paperback Version

Modeling Mindsets is also available in a paperback version.

It's a small and handy book.

A perfect traveling companion, but also great as a gift for colleagues and peers.

Interpretable Machine Learning is a comprehensive guide to making machine learning models interpretable

"Pretty convinced this is the best book out there on the subject"

– Brian Lewis, Data Scientist at Cornerstone Research

Summary

This book covers a range of interpretability methods, from inherently interpretable models to methods that can make any model interpretable, such as SHAP, LIME, and permutation feature importance. It also includes interpretation methods specific to deep neural networks and discusses why interpretability is important in machine learning. 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?

"What I love about this book is that it starts with the big picture instead of diving immediately into the nitty gritty of the methods (although all of that is there, too)."

– Andrea Farnham, Researcher at Swiss Tropical and Public Health Institute

Who the book is for

This book is essential for machine learning practitioners, data scientists, statisticians, and anyone interested in making their machine learning models interpretable. It will help readers select and apply the appropriate interpretation method for their specific project.

"This one has been a life saver for me to interpret models. ALE plots are just too good!"

– Sai Teja Pasul, Data Scientist at Kohl's

You'll learn about

• The concepts of machine leaning interpretability
• Inherently interpretable models
• Methods to make any machine model interpretable, such as SHAP, LIME and permutation feature importance
• Interpretation methods specific to deep neural networks
• Why interpretability is important and what's behind this concept

About the author

The author, Christoph Molnar, is an expert in machine learning and statistics, with a Ph.D. in interpretable machine learning.

Other Versions

The print version can be bought on Amazon.

A free HTML version of the book can be found at: https://christophm.github.io/interpretable-ml-book/