Proud to Publish: Random Forests with R

By: Diana Petrowicz, Tue Mar 2 2021
Diana Petrowicz

Author: Diana Petrowicz

In our Proud to Publish series we talk to our book editors about the books they were most proud to publish, commission or work on in the past year. For this blog post we interviewed our Senior Editor, Statistics Books Group Leader, Dr. Eva Hiripi, who talks to us about the book she is most proud to have published in 2020 and the reasons behind it. Read on to dive into the world of statistics books at Springer Nature.

© Springer Nature 2020
What book are you proud to have published in 2020 and why?

It is not easy to pick one book, because there were several great books published in my program I am very proud of. After some thinking I decided on the book “Random Forests with R”- and here is why: This book by Robin Genuer and Jean-Michel Poggi is an application focused overview of Random Forests: a statistical learning method, now among the preferred techniques in the toolbox of statisticians and data scientists.

I loved this project because of its relevant and very 21st century topic made easily accessible for the readers by the authors. As one of the reviewers wrote about the book: “Great topic, perfect for data science curricula. Clear, logical, simple.” Throughout the book, each result is given together with the code (written in R) that can be used to reproduce it.

Why is this method called “Random Forests”?

In real life, trees can form a forest. A Random Forest consists of Decision Trees bundled together, thereby building a “forest” by combining multiple models into an ensemble. Random Forest classification models have a high-prediction accuracy and run efficiently on large data bases. They can be used in very diverse fields, such as predicting drug response in a cell line or recognizing handwritten digits.

What else should we know about this book series where this book was published?

The Random Forests book is part of the “use R!” series. Books in this series demonstrate the use of R, one of the most important programming languages for statistical computing. The Statistics Books team published very diverse books in the series recently: a book on the statistical analysis of Network Data; another one on modeling MRI data and one of the most recently published books is on Retirement Income Recipes - all with the help of R.

In this complicated year, the Random Forests book project was a lot of fun. Thanks first of all to the authors, as well as to manuscript reviewers, series editors and production colleagues!

Random forests with R

Click here for more information about this and other titles in our Mathematics & Statistics collection. To add this title to your library contact us here.

If you are interested in publishing Statistics books with us, please contact Dr. Eva Hiripi.

About this book series

This series of inexpensive and focused books on R is aimed at practitioners. Books can discuss the use of R in a particular subject area (e.g., epidemiology, econometrics, psychometrics) or as it relates to statistical topics (e.g., missing data, longitudinal data).

About the authors

Robin Genuer

Robin Genuer is an Assistant Professor of Statistics at the University of Bordeaux and a member of the Inserm U1219 and Inria Bordeaux Sud-Ouest research centres.

Jean-Michel Poggi

Jean-Michel Poggi is a Professor of Statistics at the University of Paris and member of the LMO, the Orsay Mathematics Laboratory (University of Paris Saclay).

They have both produced various research works on random forests and have given numerous lectures and talks on the subject. They have also taught postgraduate and doctoral courses for a variety of audiences. Lastly, they have developed the R package VSURF.

Diana Petrowicz

Author: Diana Petrowicz

Diana Petrowicz is an Online Marketing Manager in the Institutional Marketing team, based in the London office. She manages 'The Link' blog, creates web content for the librarian webpage and produces the Library Link newsletter to keep the librarian community updated on trends and news.