An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) - Hardcover

James, Gareth; Witten, Daniela; Hastie, Trevor; Tibshirani, Robert

Hardcover

ISBN 10: 1461471370 ISBN 13: 9781461471370

Verlag: Springer-Verlag New York Inc., 2017

Inhaltsangabe

<P><B>AN INTRODUCTION TO STATISTICAL LEARNING</B> PROVIDES AN ACCESSIBLE OVERVIEW OF THE FIELD OF STATISTICAL LEARNING, AN ESSENTIAL TOOLSET FOR MAKING SENSE OF THE VAST AND COMPLEX DATA SETS THAT HAVE EMERGED IN FIELDS RANGING FROM BIOLOGY TO FINANCE TO MARKETING TO ASTROPHYSICS IN THE PAST TWENTY YEARS. THIS BOOK PRESENTS SOME OF THE MOST IMPORTANT MODELING AND PREDICTION TECHNIQUES, ALONG WITH RELEVANT APPLICATIONS. TOPICS INCLUDE LINEAR REGRESSION, CLASSIFICATION, RESAMPLING METHODS, SHRINKAGE APPROACHES, TREE-BASED METHODS, SUPPORT VECTOR MACHINES, CLUSTERING, AND MORE. COLOR GRAPHICS AND REAL-WORLD EXAMPLES ARE USED TO ILLUSTRATE THE METHODS PRESENTED. SINCE THE GOAL OF THIS TEXTBOOK IS TO FACILITATE THE USE OF THESE STATISTICAL LEARNING TECHNIQUES BY PRACTITIONERS IN SCIENCE, INDUSTRY, AND OTHER FIELDS, EACH CHAPTER CONTAINS A TUTORIAL ON IMPLEMENTING THE ANALYSES AND METHODS PRESENTED IN R, AN EXTREMELY POPULAR OPEN SOURCE STATISTICAL SOFTWARE PLATFORM.</P><P>TWO OF THE AUTHORS CO-WROTE THE ELEMENTS OF STATISTICAL LEARNING (HASTIE, TIBSHIRANI AND FRIEDMAN, 2ND EDITION 2009), A POPULAR REFERENCE BOOK FOR STATISTICS AND MACHINE LEARNING RESEARCHERS. <B>AN INTRODUCTION TO STATISTICAL LEARNING</B> COVERS MANY OF THE SAME TOPICS, BUT AT A LEVEL ACCESSIBLE TO A MUCH BROADER AUDIENCE. THIS BOOK IS TARGETED AT STATISTICIANS AND NON-STATISTICIANS ALIKE WHO WISH TO USE CUTTING-EDGE STATISTICAL LEARNING TECHNIQUES TO ANALYZE THEIR DATA. THE TEXT ASSUMES ONLY A PREVIOUS COURSE IN LINEAR REGRESSION AND NO KNOWLEDGE OF MATRIX ALGEBRA.</P>

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Críticas

"An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of what methods are available, and when to use them, including explicit R code. Anyone who wants to intelligently analyze complex data should own this book." (Larry Wasserman, Professor, Department of Statistics and Machine Learning Department, Carnegie Mellon University)

Reseña del editor

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Details zur Ausgabe

• Verlag: Springer-Verlag New York Inc.
• Erscheinungsdatum: 2017
• ISBN 10: 1461471370
• ISBN 13: 9781461471370
• Einband: Tapa dura
• Sprache: Englisch
• Anzahl der Seiten: 426