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000945508 019__ $$a1197839472
000945508 020__ $$a9783030564858$$q(electronic book)
000945508 020__ $$a3030564851$$q(electronic book)
000945508 020__ $$z9783030564841
000945508 0247_ $$a10.1007/978-3-030-56485-8$$2doi
000945508 0247_ $$a10.1007/978-3-030-56
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000945508 050_4 $$aQA276.45.R3
000945508 08204 $$a519.50285$$223
000945508 1001_ $$aGenuer, Robin.
000945508 24010 $$aForêts aléatoires avec R.$$lEnglish
000945508 24510 $$aRandom forests with R /$$cRobin Genuer, Jean-Michel Poggi.
000945508 260__ $$aCham :$$bSpringer,$$c2020.
000945508 300__ $$a1 online resource (107 pages).
000945508 336__ $$atext$$btxt$$2rdacontent
000945508 337__ $$acomputer$$bc$$2rdamedia
000945508 338__ $$aonline resource$$bcr$$2rdacarrier
000945508 4901_ $$aUse R! .
000945508 504__ $$aIncludes bibliographical references and index.
000945508 506__ $$aAccess limited to authorized users.
000945508 520__ $$aThis 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.
000945508 588__ $$aDescription based on print version record.
000945508 650_0 $$aMathematical statistics.
000945508 650_0 $$aR (Computer program language)
000945508 7001_ $$aPoggi, Jean-Michel,$$d1960-
000945508 77608 $$iPrint version:$$aGenuer, Robin$$tRandom Forests with R$$dCham : Springer International Publishing AG,c2020$$z9783030564841
000945508 830_0 $$aUse R.
000945508 85280 $$bebk$$hSpringerLink
000945508 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-56485-8$$zOnline Access$$91397441.1
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000945508 980__ $$aEBOOK
000945508 980__ $$aBIB
000945508 982__ $$aEbook
000945508 983__ $$aOnline
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