An introduction to statistical learning : with applications in R (Record no. 2904)

000 -LEADER
fixed length control field 01993nam a22002537a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20241113111816.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 191210b ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781461471370
040 ## - CATALOGING SOURCE
Transcribing agency Tata Book House
Original cataloging agency ICTS-TIFR
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA 276
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name James Gareth
245 ## - TITLE STATEMENT
Title An introduction to statistical learning : with applications in R
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New York:
Name of publisher, distributor, etc. Springer,
Date of publication, distribution, etc. [c2013]
300 ## - Physical Description
Pages: 426 p
490 ## - SERIES STATEMENT
Series statement Springer Texts in Statistics
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note Ch 1. Introduction<br/>Ch 2. Statistical Learning<br/>Ch 3. Linear Regression<br/>Ch 4. Classification<br/>Ch 5. Resampling Methods<br/>Ch 6. Linear Model Selection and Regularization<br/>Ch 7. Moving Beyond Linearity <br/>Ch 8. Tree-Based Methods<br/>Ch 9. Support Vector Machines<br/>Ch 10. Unsupervised Learning
520 ## - SUMMARY, ETC.
Summary, etc. 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. --- summary provided by publisher
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Statistics
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Daniela Witten
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Trevor Hastie
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Robert Tibshirani
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Book
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Home library Shelving location Date acquired Full call number Accession No. Koha item type
          ICTS Rack No 5 12/10/2019 QA 276 02259 Book