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1. |
Record Nr. |
UNINA9910863167703321 |
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Autore |
Genuer Robin |
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Titolo |
Random Forests with R / / by Robin Genuer, Jean-Michel Poggi |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (X, 98 p. 49 illus., 5 illus. in color.) |
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Collana |
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Disciplina |
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Soggetti |
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Statistics |
Big data |
Bioinformatics |
Biometry |
Social sciences - Statistical methods |
Statistical Theory and Methods |
Big Data |
Biostatistics |
Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- CART trees -- Random forests -- Variable importance -- Variable selection -- References. |
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Sommario/riassunto |
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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 |
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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. . |
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2. |
Record Nr. |
UNINA9911019806603321 |
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Titolo |
Modeling and simulation fundamentals : theoretical underpinnings and practical domains / / edited by John A. Sokolowski, Catherine M. Banks |
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Pubbl/distr/stampa |
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Hoboken, N.J., : Wiley, c2010 |
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ISBN |
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9786612707568 |
9781282707566 |
1282707566 |
9780470590621 |
0470590629 |
9780470590614 |
0470590610 |
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Descrizione fisica |
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1 online resource (453 p.) |
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Altri autori (Persone) |
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SokolowskiJohn A. <1953-> |
BanksCatherine M. <1960-> |
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Disciplina |
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Soggetti |
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Mathematical models |
Mathematical optimization |
Simulation methods |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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MODELING 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 |
Modeling 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; |
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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 |
8 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 |
VV&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 |
ReferencesFurther Readings; Index |
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Sommario/riassunto |
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An 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 |
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