03611nam 2200493 450 991048357160332120220623181642.03-030-56485-110.1007/978-3-030-56485-8(CKB)4100000011435818(DE-He213)978-3-030-56485-8(MiAaPQ)EBC6348281(PPN)250221268(EXLCZ)99410000001143581820210216d2020 uy 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierRandom forests with R /Robin Genuer, Jean-Michel Poggi1st ed. 2020.Cham, Switzerland :Springer,[2020]©20201 online resource (X, 98 p. 49 illus., 5 illus. in color.)Use R!,2197-57363-030-56484-3 Introduction -- CART trees -- Random forests -- Variable importance -- Variable selection -- References.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. .Use R!,2197-5736Mathematical statisticsR (Computer program language)Mathematical statistics.R (Computer program language).519.5Genuer Robin1015115Poggi Jean-Michel1960-MiAaPQMiAaPQMiAaPQBOOK9910483571603321Random forests with R2368791UNINA01299cam a2200277 i 4500991000152129707536220406s2000 caua 000 0 eng d8883311272b14435056-39ule_instBibl. Dip.le Aggr. Ingegneria Innovazione - Sez. Ingegneria Innovazioneeng006.786921Gross, Phil147131Director 8 :la grande guida, la guida ufficiale a Director 8, Shockwave, Internet, Studio /Phil Gross, Jason RobertsMilano :Mondadoro Informatica,2000xxvii, 989 p. :ill. ;23 cm. +1 CD-ROMIncludes indexDirector (Computer file)Interactive multimedia.Roberts, Jasonauthorhttp://id.loc.gov/vocabulary/relators/aut1215035.b1443505606-04-2206-04-22991000152129707536LE026 006.7869 GRO 01.02 200012026000134833le026Testo collocato sul pavimento.Fondo GISIpE0.00-lt 00000.i1600611206-04-22LE026 cd-rom n. 444cd12026000134826le026Fondo GISIpE0.00-l- 00000.i1600612406-04-22Director 82806963UNISALENTOle02606-04-22ma engcau00