An introduction to statistical learning : with applications in R / Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
2021
QA276 .J36 2021
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Online Access
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Authorized users
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Can lend chapters, not whole ebooks
Details
Title
An introduction to statistical learning : with applications in R / Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
Edition
Second edition.
ISBN
9781071614181 (electronic bk.)
1071614185 (electronic bk.)
1071614177
9781071614174
9781071614174
1071614177
1071614185 (electronic bk.)
1071614177
9781071614174
9781071614174
1071614177
Published
New York : Springer, [2021]
Copyright
©2021
Language
English
Description
1 online resource : illustrations (chiefly color)
Other Standard Identifiers
9781071614174
10.1007/978-1-0716-1418-1 doi
10.1007/978-1-0716-1418-1 doi
Call Number
QA276 .J36 2021
Dewey Decimal Classification
519.5
Summary
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, deep learning, survival analysis, multiple testing, 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. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
Note
Previous edition: New York: Springer, 2013.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Description based on print version record.
Series
Springer texts in statistics.
Available in Other Form
Introduction to statistical learning.
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
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All Resources
Table of Contents
Preface
1 Introduction
2 Statistical Learning
3 Linear Regression
4 Classification
5 Resampling Methods
6 Linear Model Selection and Regularization
7 Moving Beyond Linearity
8 Tree-Based Methods
9 Support Vector Machines
10 Deep Learning
11 Survival Analysis and Censored Data
12 Unsupervised Learning
13 Multiple Testing
Index.
1 Introduction
2 Statistical Learning
3 Linear Regression
4 Classification
5 Resampling Methods
6 Linear Model Selection and Regularization
7 Moving Beyond Linearity
8 Tree-Based Methods
9 Support Vector Machines
10 Deep Learning
11 Survival Analysis and Censored Data
12 Unsupervised Learning
13 Multiple Testing
Index.