LEADER 04318nam 22006975 450 001 9910863167703321 005 20250325154436.0 010 $a9783030564858 010 $a3030564851 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 $a20200910d2020 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRandom Forests with R /$fby Robin Genuer, Jean-Michel Poggi 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (X, 98 p. 49 illus., 5 illus. in color.) 225 1 $aUse R!,$x2197-5744 311 08$a9783030564841 311 08$a3030564843 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!,$x2197-5744 606 $aStatistics 606 $aBig data 606 $aBioinformatics 606 $aBiometry 606 $aSocial sciences$xStatistical methods 606 $aStatistical Theory and Methods 606 $aBig Data 606 $aBioinformatics 606 $aBiostatistics 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 615 0$aStatistics. 615 0$aBig data. 615 0$aBioinformatics. 615 0$aBiometry. 615 0$aSocial sciences$xStatistical methods. 615 14$aStatistical Theory and Methods. 615 24$aBig Data. 615 24$aBioinformatics. 615 24$aBiostatistics. 615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 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 $a9910863167703321 996 $aRandom forests with R$92368791 997 $aUNINA