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.

Introduction To Conformal Prediction With Python is the quickest way to learn an easy-to-use and very general technique for uncertainty quantification.

"This concise book is accessible, lucid, and full of helpful code snippets. It explains the mathematical ideas with clarity and provides the reader with practical examples that illustrate the essence of conformal prediction, a powerful idea for uncertainty quantification."

– Junaid Butt, Research Software Engineer, IBM Research

Modern statistics can be a difficult topic, but Christoph has managed to make it feel easy, practical, and fun! Reading this book is a great first step towards gaining mastery of conformal prediction and related topics.

– Anastasios Angelopoulos, Researcher at the University of California, Berkeley

Summary

A prerequisite for trust in machine learning is uncertainty quantification.  Without it, an accurate prediction and a wild guess look the same.

Yet many machine learning models come without uncertainty quantification. And while there are many approaches to uncertainty – from Bayesian posteriors to bootstrapping – we have no guarantees that these approaches will perform well on new data.

I really enjoyed reading the book. The data science and machine learning community needs more people like Christoph Molnar who are able to translate emerging breakthrough research into digestible concepts. I can see this book becoming a key piece in accelerating the rate of adoption of conformal ML.

– Guilherme Del Nero Maia, Principal Data Science at Jabil

At first glance conformal prediction seems like yet another contender. But conformal prediction can work in combination with any other uncertainty approach and has many advantages that make it stand out:

• Guaranteed coverage: Prediction regions generated by conformal prediction come with coverage guarantees of the true outcome
• Easy to use: Conformal prediction approaches can be implemented from scratch with just a few lines of code
• Model-agnostic: Conformal prediction works with any machine learning model
• Distribution-free: Conformal prediction makes no distributional assumptions
• No retraining required: Conformal prediction can be used without retraining the model
• Broad application: conformal prediction works for classification, regression, time series forecasting, and many other tasks

Sound good? 

Then this is the right book for you to learn about this versatile, easy-to-use yet powerful tool for taming the uncertainty of your models.

"Great practical examples, easy explanations, and highly entertaining. If you want to learn about the best Uncertainty Quantification framework for the 21st century, don't miss out on this book."

– Valeriy Manokhin, Managing Director at Open Predictive Technologies & Creator of Awesome Conformal Prediction

This book:

• Teaches the intuition behind conformal prediction
• Demonstrates how conformal prediction works for classification and regression
• Shows how to apply conformal prediction using Python and MAPIE
• Enables you to quickly learn new conformal algorithms

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.

With the knowledge in this book, you'll be ready to quantify the uncertainty of any model.

This book is a comprehensive guide and resource for anyone who wants to learn how to quantify uncertainty with conformal prediction by using python. Christoph's writing is clear and engaging. He provides practical examples that help readers understand how to apply conformal prediction techniques/concepts to real-world problems.

– Tony Zhang, Data Scientist at Munich Re

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/