Brad Boehmke - Hands-On Machine Learning with R

Chapman & Hall/CRC
The R Series

Series Editors

John M. Chambers, Department of Statistics, Stanford University, California, USA

Torsten Hothorn, Division of Biostatistics, University of Zurich, Switzerland

Duncan Temple Lang, Department of Statistics, University of California, Davis, USA

Hadley Wickham, RStudio, Boston, Massachusetts, USA

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Hands-On Machine Learning with R

Brad Boehmke
Brandon Greenwell

CRC Press

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Brad:

To Kate, Alivia, and Jules for making sure I have a life outside of programming and to my mother who, undoubtedly, will try to read the pages that follow.

Brandon:

To my parents for encouragement, to Thaddeus Tarpey for inspiration, and to Julia, Lilly, and Jen for putting up with me while writing this book.

Welcome to Hands-On Machine Learning with R. This book provides hands-on modules for many of the most common machine learning methods to include:

Generalized low rank models

Clustering algorithms

Autoencoders

Regularized models

Random forests

Gradient boosting machines

Deep neural networks

Stacking / super learners

and more!

You will learn how to build and tune these various models with R packages that have been tested and approved due to their ability to scale well. However, our motivation in almost every case is to describe the techniques in a way that helps develop intuition for its strengths and weaknesses. For the most part, we minimize mathematical complexity when possible but also provide resources to get deeper into the details if desired.

We intend this work to be a practitioners guide to the machine learning process and a place where one can come to learn about the approach and to gain intuition about the many commonly used, modern, and powerful methods accepted in the machine learning community. If you are familiar with the analytic methodologies, this book may still serve as a reference for how to work with the various R packages for implementation. While an abundance of videos, blog posts, and tutorials exist online, we have long been frustrated by the lack of consistency, completeness, and bias towards singular packages for implementation. This is what inspired this book.

This book is not meant to be an introduction to R or to programming in general; as we assume the reader has familiarity with the R language to include defining functions, managing R objects, controlling the flow of a program, and other basic tasks. If not, we would refer you to R for Data Science (Goodfellow et al., 2016)).