LEADER 00576nam 2200169z- 450 001 9910712385803321 035 $a(CKB)5470000002492271 035 $a(EXLCZ)995470000002492271 100 $a20230509c2015uuuu -u- - 101 0 $aeng 200 10$aSmwrBase : an R package for managing hydrologic data, version 1.1.1 210 $cU.S. Department of the Interior, U.S. Geological Survey$aReston, Virginia 517 $aSmwrBase 906 $aBOOK 912 $a9910712385803321 996 $aSmwrBase : an R package for managing hydrologic data, version 1.1.1$93334658 997 $aUNINA 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 LEADER 04884nam 2200685Ia 450 001 9911019806603321 005 20200520144314.0 010 $a9786612707568 010 $a9781282707566 010 $a1282707566 010 $a9780470590621 010 $a0470590629 010 $a9780470590614 010 $a0470590610 035 $a(CKB)2670000000034227 035 $a(EBL)564928 035 $a(OCoLC)654787157 035 $a(SSID)ssj0000423325 035 $a(PQKBManifestationID)11291584 035 $a(PQKBTitleCode)TC0000423325 035 $a(PQKBWorkID)10439277 035 $a(PQKB)10199291 035 $a(MiAaPQ)EBC564928 035 $a(Perlego)2771546 035 $a(EXLCZ)992670000000034227 100 $a20090914d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aModeling and simulation fundamentals $etheoretical underpinnings and practical domains /$fedited by John A. Sokolowski, Catherine M. Banks 210 $aHoboken, N.J. $cWiley$dc2010 215 $a1 online resource (453 p.) 300 $aDescription based upon print version of record. 311 08$a9780470486740 311 08$a0470486740 320 $aIncludes bibliographical references and index. 327 $aMODELING AND SIMULATION FUNDAMENTALS; CONTENTS; Preface; Contributors; 1 Introduction to Modeling and Simulation; M&S; M&S Characteristics and Descriptors; M&S Categories; Conclusion; References; 2 Statistical Concepts for Discrete Event Simulation; Probability; Simulation Basics; Input Data Modeling; Output Data Analysis; Conclusion; References; 3 Discrete-Event Simulation; Queuing System Model Components; Simulation Methodology; DES Example; Hand Simulation-Spreadsheet Implementation; Arena Simulation; Conclusion; References; 4 Modeling Continuous Systems; System Class 327 $aModeling and Simulation (M&S) StrategyModeling Approach; Model Examples; Simulating Continuous Systems; Simulation Implementation; Conclusion; References; 5 Monte Carlo Simulation; The Monte Carlo Method; Sensitivity Analysis; Conclusion; References; 6 Systems Modeling: Analysis and Operations Research; System Model Types; Modeling Methodologies and Tools; Analysis of Modeling and Simulation (M&S); OR Methods; Conclusion; References; Further Readings; 7 Visualization; Computer Graphics Fundamentals; Visualization Software and Tools; Case Studies; Conclusion; References 327 $a8 M&S Methodologies: A Systems Approach to the Social SciencesSimulating State and Substate Actors with CountrySim: Synthesizing Theories Across the Social Sciences; The CountrySim Application and Sociocultural Game Results; Conclusions and the Way Forward; References; 9 Modeling Human Behavior; Behavioral Modeling at the Physical Level; Behavioral Modeling at the Tactical and Strategic Level; Techniques for Human Behavior Modeling; Human Factors; Human-Computer Interaction; Conclusion; References; 10 Verification, Validation, and Accreditation; Motivation; Background Definitions 327 $aVV&A DefinitionsV&V as Comparisons; Performing VV&A; V&V Methods; VV&A Case Studies; Conclusion; Acknowledgments; References; 11 An Introduction to Distributed Simulation; Trends and Challenges of Distributed Simulation; A Brief History of Distributed Simulation; Synchronization Algorithms for Parallel and Distributed Simulation; Distributed Simulation Middleware; Conclusion; References; 12 Interoperability and Composability; Defining Interoperability and Composability; Current Interoperability Standard Solutions; Engineering Methods Supporting Interoperation and Composition; Conclusion 327 $aReferencesFurther Readings; Index 330 $aAn insightful presentation of the key concepts, paradigms, and applications of modeling and simulation Modeling and simulation has become an integral part of research and development across many fields of study, having evolved from a tool to a discipline in less than two decades. Modeling and Simulation Fundamentals offers a comprehensive and authoritative treatment of the topic and includes definitions, paradigms, and applications to equip readers with the skills needed to work successfully as developers and users of modeling and simulation. Featuring contributions written b 606 $aMathematical models 606 $aMathematical optimization 606 $aSimulation methods 615 0$aMathematical models. 615 0$aMathematical optimization. 615 0$aSimulation methods. 676 $a511/.8 701 $aSokolowski$b John A.$f1953-$01630190 701 $aBanks$b Catherine M.$f1960-$01630191 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019806603321 996 $aModeling and simulation fundamentals$94421045 997 $aUNINA