## An introduction to statistical learning : with applications in R / Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.

Series: Springer texts in statisticsPublisher: New York : Springer : Springer Science+Business Media, 2015Copyright date: 2013Edition: Corrected at 8th printing 2017Description: xiv, 426 pages : illustrations (chiefly color) ; 25 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781461471370; 1461471370; 9781461471387; 1461471389Other title: Statistical learningSubject(s): Mathematical statistics | Mathematical models | Mathematical statistics -- Problems, exercises, etc | Mathematical models -- Problems, exercises, etc | R (Computer program language) | Statistics | Models, Statistical | Statistics as Topic | Mathematical models | Mathematical statistics | R (Computer program language) | StatisticsGenre/Form: Problems and exercises.DDC classification: 519.5 LOC classification: QA276 | .I58 2015NLM classification: QA276Other classification: 85.03Item type | Current library | Call number | Copy number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|

BOOK | NCAR Library Mesa Lab | QA276 .I585 2013 | 1 | Checked out | 10/01/2023 | 50583020007237 | |

BOOK | NCAR Library Mesa Lab | QA276 .I585 2017 | 2 | Available | 50583020009753 | ||

BOOK | NCAR Library Mesa Lab | QA276 .I585 2013 | 3 | Available | 50583020004192 | ||

BOOK | NCAR Library Mesa Lab | QA276 .I585 2013 | 4 | Lost | 50583020001669 |

Includes index.

Introduction -- Statistical learning -- Linear regression -- Classification -- Resampling methods -- Linear model selection and regularization -- Moving beyond linearity -- Tree-based methods -- Support vector machines -- Unsupervised learning.

"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. Provides tools for Statistical Learning that are essential for practitioners in science, industry and other fields. Analyses and methods are presented in R. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering. Extensive use of color graphics assist the reader"--Publisher description.