Merge remote-tracking branch 'refs/remotes/origin/master'

manuscript/00.0-preface.Rmd

@@ -74,7 +74,7 @@ After I finished my master's degree in statistics, I decided not to pursue a PhD
 Writing just stressed me out too much.
 So I took jobs as data scientist in a Fintech start-up and as statistician in clinical research.
 After these three years in industry I started writing this book and a few months later I started a PhD in interpretable machine learning.
-By starting this book, I regained the joy of writing and it helped me to develop a passion for research.
+While working on this book, I rediscovered the joy of writing and it helped me to develop a passion for research.
 
 This book covers many techniques of interpretable machine learning.
 In the first chapters, I introduce the concept of interpretability and motivate why interpretability is necessary.
@@ -83,7 +83,7 @@ The book discusses the different properties of explanations and what humans thin
 Then we will discuss machine learning models that are inherently interpretable, for example regression models and decision trees.
 The main focus of this book is on model-agnostic interpretability methods.
 Model-agnostic means that these methods can be applied to any machine learning model and are applied after the model has been trained.
-The independence of the model makes model-agnostic methods very flexible and powerful.
+This independence from the model makes model-agnostic methods very flexible and powerful.
 Some techniques explain how individual predictions were made, like local interpretable model-agnostic explanations (LIME) and Shapley values.
 Other techniques describe the average behavior of the model across a dataset.
 Here we learn about the partial dependence plot, accumulated local effects, permutation feature importance and many other methods.
@@ -109,20 +109,20 @@ Machine learning has received great attention from many people in research and i
 Sometimes machine learning is overhyped in the media, but there are many real and impactful applications.
 Machine learning is a powerful technology for products, research and automation.
 Today, machine learning is used, for example, to detect fraudulent financial transactions, recommend movies and classify images.
-It is often crucial that the machine learning models are interpretable.
-Interpretability helps the developer to debug and improve the model, build trust in the model, justify model predictions and gain insights.
+It is often crucial that machine learning models are interpretable.
+Interpretability helps developers with debugging and improvements, builds trust in the model, justifies model predictions and leads to new insights.
 The increased need for machine learning interpretability is a natural consequence of an increased use of machine learning.
 This book has become a valuable resource for many people.
 Teaching instructors use the book to introduce their students to the concepts of interpretable machine learning.
-I received e-mails from various master and doctoral students who told me that this book was the starting point and most important reference for their theses.
-The book has helped applied researchers in the field of ecology, finance, psychology, etc. who use machine learning to understand their data.
+I have received e-mails from several Master's students and Ph.D. students who told me that this book was the starting point and most important reference for their theses.
+The book has helped applied researchers in the fields of ecology, finance, psychology, etc. who use machine learning to understand their data.
 Data scientists from industry told me that they use the "Interpretable Machine Learning" book for their work and recommend it to their colleagues.
-I am happy that many people can benefit from this book and become experts in model interpretation.
+I am happy that many people benefited from this book and become experts in model interpretation.
 
 I would recommend this book to practitioners who want an overview of techniques to make their machine learning models more interpretable.
-It is also recommended to students and researchers (and anyone else) who is interested in the topic.
-To benefit from this book, you should already have a basic understanding of machine learning.
-You should also have a mathematical understanding at university entry level to be able to follow the theory and formulas in this book.
+It would also prove beneficial to students and researchers (and anyone else) who is interested in the topic.
+To make the most out of this book, you should have a basic understanding of machine learning.
+You should also have an understanding of entry level university mathematics to be able to follow the theory and formulas in this book.
 It should also be possible, however, to understand the intuitive description of the method at the beginning of each chapter without mathematics.
 
 I hope you enjoy the book!

manuscript/01-introduction.Rmd

@@ -7,7 +7,7 @@ This book explains to you how to make (supervised) machine learning models inter
 The chapters contain some mathematical formulas, but you should be able to understand the ideas behind the methods even without the formulas.
 This book is not for people trying to learn machine learning from scratch.
 If you are new to machine learning, there are a lot of books and other resources to learn the basics.
-I recommend the book "The Elements of Statistical Learning" by Hastie, Tibshirani, and Friedman (2009) [^Hastie] and [Andrew Ng's "Machine Learning" online course](https://www.coursera.org/learn/machine-learning) on the online learning platform coursera.com to start with machine learning.
+I recommend the book ["The Elements of Statistical Learning" by Hastie, Tibshirani, and Friedman (2009)](https://hastie.su.domains/ElemStatLearn/) [^Hastie] and [Andrew Ng's "Machine Learning" online course](https://www.coursera.org/learn/machine-learning) on the online learning platform coursera.com to start with machine learning.
 Both the book and the course are available free of charge!
 
 New methods for the interpretation of machine learning models are published at breakneck speed.
@@ -18,12 +18,12 @@ Internalizing the basic concepts also empowers you to better understand and eval
 
 This book starts with some (dystopian) [short stories](#storytime) that are not needed to understand the book, but hopefully will entertain and make you think.
 Then the book explores the concepts of [machine learning interpretability](#interpretability).
-We will discuss when interpretability is important and what different types of explanations there are.
+We will discuss when interpretability is important and the different types of explanations that exist.
 Terms used throughout the book can be looked up in the [Terminology chapter](#terminology).
 Most of the models and methods explained are presented using real data examples which are described in the [Data chapter](#data).
 One way to make machine learning interpretable is to use [interpretable models](#simple), such as linear models or decision trees.
 The other option is the use of [model-agnostic interpretation tools](#agnostic) that can be applied to any supervised machine learning model.
-Model-agnostic methods can be divided [global methods](#global-methods) that describe the average behavior of the model and [local methods](#local-methods) that explain individual predictions.
+Model-agnostic methods can be divided into [global methods](#global-methods) that describe the average behavior of the model, and [local methods](#local-methods) that explain individual predictions.
 The Model-Agnostic Methods chapter deals with methods such as [partial dependence plots](#pdp) and [feature importance](#feature-importance).
 Model-agnostic methods work by changing the input of the machine learning model and measuring changes in the prediction output.
 The book ends with an optimistic outlook on what [the future of interpretable machine learning](#future) might look like.
@@ -32,4 +32,4 @@ You can either read the book from beginning to end or jump directly to the metho
 
 I hope you will enjoy the read!
 
-[^Hastie]: Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. "The elements of statistical learning". www.web.stanford.edu/~hastie/ElemStatLearn/ (2009).
+[^Hastie]: Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. "The elements of statistical learning". hastie.su.domains/ElemStatLearn (2009).