Merge pull request #376 from Etrama/preface_lang

00.0-preface lang fix

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!