Picture 1 of 1
Gallery
Picture 1 of 1
Have one to sell?
Gareth James Trevor Hastie Robert Tibshirani An Introduction to Stati (Hardback)
Another great item from Rarewaves USA | Free delivery!
US $127.79
ApproximatelyS$ 163.75
Condition:
Brand New
A new, unread, unused book in perfect condition with no missing or damaged pages.
Oops! Looks like we're having trouble connecting to our server.
Refresh your browser window to try again.
Shipping:
Free Economy Shipping.
Located in: Oswego, Illinois, United States
Delivery:
Estimated between Wed, 27 Aug and Wed, 3 Sep to 94104
Returns:
30 days return. Buyer pays for return shipping. If you use an eBay shipping label, it will be deducted from your refund amount.
Coverage:
Read item description or contact seller for details. See all detailsSee all details on coverage
(Not eligible for eBay purchase protection programmes)
Shop with confidence
Seller assumes all responsibility for this listing.
eBay item number:297359789051
Item specifics
- Condition
- Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
- Book Title
- An Introduction to Statistical Learning
- Subtitle
- with Applications in R
- EAN
- 9781071614174
- ISBN
- 9781071614174
- Edition
- Second Edition 2021
- Release Date
- 07/30/2021
- Genre
- Computing & Internet
- Topic
- Science Nature & Math
- Title
- An Introduction to Statistical Learning
- Release Year
- 2021
- Item Height
- 235mm
- Country/Region of Manufacture
- US
About this product
Product Identifiers
Publisher
Springer
ISBN-10
1071614177
ISBN-13
9781071614174
eBay Product ID (ePID)
17050082535
Product Key Features
Number of Pages
Xv, 607 Pages
Publication Name
Introduction to Statistical Learning : with Applications in R
Language
English
Publication Year
2021
Subject
Mathematical & Statistical Software, Probability & Statistics / General, Intelligence (Ai) & Semantics, General
Type
Textbook
Subject Area
Mathematics, Computers
Series
Springer Texts in Statistics Ser.
Format
Hardcover
Dimensions
Item Weight
42 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Edition Number
2
Dewey Edition
23
TitleLeading
An
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
519.5
Table Of Content
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.
Synopsis
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 na ve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility., 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 naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
LC Classification Number
QA276-280
Item description from the seller
Seller feedback (532,260)
- d***o (1431)- Feedback left by buyer.Past monthVerified purchaseItem as described…thanks
- d***o (1431)- Feedback left by buyer.Past monthVerified purchaseItem as described..thanks
- a***g (1293)- Feedback left by buyer.Past monthVerified purchaseThank you
Seller feedback (532,260)
- d***o (1431)- Feedback left by buyer.Past monthVerified purchaseItem as described…thanks
- d***o (1431)- Feedback left by buyer.Past monthVerified purchaseItem as described..thanks
- a***g (1293)- Feedback left by buyer.Past monthVerified purchaseThank you