Random forests with R / Robin Genuer, Jean-Michel Poggi.
2020
QA276.45.R3
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Title
Random forests with R / Robin Genuer, Jean-Michel Poggi.
Author
Genuer, Robin.
Uniform Title
ForĂȘts alĂ©atoires avec R. English
ISBN
9783030564858 (electronic book)
3030564851 (electronic book)
9783030564841
3030564851 (electronic book)
9783030564841
Publication Details
Cham : Springer, 2020.
Language
English
Description
1 online resource (107 pages).
Item Number
10.1007/978-3-030-56485-8 doi
10.1007/978-3-030-56
10.1007/978-3-030-56
Call Number
QA276.45.R3
Dewey Decimal Classification
519.50285
Summary
This book offers an application-oriented guide to random forests: a statistical learning method extensively used in many fields of application, thanks to its excellent predictive performance, but also to its flexibility, which places few restrictions on the nature of the data used. Indeed, random forests can be adapted to both supervised classification problems and regression problems. In addition, they allow us to consider qualitative and quantitative explanatory variables together, without pre-processing. Moreover, they can be used to process standard data for which the number of observations is higher than the number of variables, while also performing very well in the high dimensional case, where the number of variables is quite large in comparison to the number of observations. Consequently, they are now among the preferred methods in the toolbox of statisticians and data scientists. The book is primarily intended for students in academic fields such as statistical education, but also for practitioners in statistics and machine learning. A scientific undergraduate degree is quite sufficient to take full advantage of the concepts, methods, and tools discussed. In terms of computer science skills, little background knowledge is required, though an introduction to the R language is recommended. Random forests are part of the family of tree-based methods; accordingly, after an introductory chapter, Chapter 2 presents CART trees. The next three chapters are devoted to random forests. They focus on their presentation (Chapter 3), on the variable importance tool (Chapter 4), and on the variable selection problem (Chapter 5), respectively. After discussing the concepts and methods, we illustrate their implementation on a running example. Then, various complements are provided before examining additional examples. Throughout the book, each result is given together with the code (in R) that can be used to reproduce it. Thus, the book offers readers essential information and concepts, together with examples and the software tools needed to analyse data using random forests.
Bibliography, etc. Note
Includes bibliographical references and index.
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Access limited to authorized users.
Source of Description
Description based on print version record.
Added Author
Poggi, Jean-Michel, 1960-
Series
Use R.
Available in Other Form
Random Forests with R
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