From 96de26387729983a633bcc3414ba5a05b7e94362 Mon Sep 17 00:00:00 2001 From: Etrama Date: Wed, 12 Jul 2023 14:10:10 -0400 Subject: [PATCH 1/2] 01-introduction lang fix --- manuscript/01-introduction.Rmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/manuscript/01-introduction.Rmd b/manuscript/01-introduction.Rmd index 4cbefe9f..ea24562a 100644 --- a/manuscript/01-introduction.Rmd +++ b/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)](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. +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) for people who are new to 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. From c906259a6ef10163a488d70da3504dd5c532e1a0 Mon Sep 17 00:00:00 2001 From: Etrama <22076465+Etrama@users.noreply.github.com> Date: Wed, 12 Jul 2023 14:14:43 -0400 Subject: [PATCH 2/2] Update 01-introduction.Rmd --- manuscript/01-introduction.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manuscript/01-introduction.Rmd b/manuscript/01-introduction.Rmd index ea24562a..e888af86 100644 --- a/manuscript/01-introduction.Rmd +++ b/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) for people who are new to 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.