LEADER 03611nam 2200493 450 001 9910483571603321 005 20220623181642.0 010 $a3-030-56485-1 024 7 $a10.1007/978-3-030-56485-8 035 $a(CKB)4100000011435818 035 $a(DE-He213)978-3-030-56485-8 035 $a(MiAaPQ)EBC6348281 035 $a(PPN)250221268 035 $a(EXLCZ)994100000011435818 100 $a20210216d2020 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRandom forests with R /$fRobin Genuer, Jean-Michel Poggi 205 $a1st ed. 2020. 210 1$aCham, Switzerland :$cSpringer,$d[2020] 210 4$dİ2020 215 $a1 online resource (X, 98 p. 49 illus., 5 illus. in color.) 225 1 $aUse R!,$x2197-5736 311 $a3-030-56484-3 327 $aIntroduction -- CART trees -- Random forests -- Variable importance -- Variable selection -- References. 330 $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. . 410 0$aUse R!,$x2197-5736 606 $aMathematical statistics 606 $aR (Computer program language) 615 0$aMathematical statistics. 615 0$aR (Computer program language). 676 $a519.5 700 $aGenuer$b Robin$01015115 702 $aPoggi$b Jean-Michel$f1960- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483571603321 996 $aRandom forests with R$92368791 997 $aUNINA