MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03159nam a22003137a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
ZW-GwMSU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20241008124132.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
241008b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781071614204 |
| 040 ## - CATALOGING SOURCE |
|---|
| Language of cataloging |
English |
| Transcribing agency |
MSULIB |
| Description conventions |
rda |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA76.9.M35 INT |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
James, Gareth |
| Relator term |
author |
| 245 13 - TITLE STATEMENT |
|---|
| Title |
An introduction to statistical learning : |
| Remainder of title |
with applications in R / |
| Statement of responsibility, etc |
created by Gareth James , Daniela Witten, Trevor Hastie and Robert Tibshirani |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
Second edition |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Name of producer, publisher, distributor, manufacturer |
Springer, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2021 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xv, 607 pages : |
| Other physical details |
illustrations (some colored) ; |
| Dimensions |
24 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| source |
rdamedia |
| media type term |
unmediated |
| media type code |
n |
| 338 ## - CARRIER TYPE |
|---|
| source |
rdacarrier |
| carrier type term |
volume |
| carrier type code |
nc |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes index |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Introduction Statistical learning Linear regression Classification Resampling methods Linear model selection and regularization Moving beyond linearity Tree-based methods Support vector machines Deep learning Survival analysis and censored data Unsupervised learning Multiple testing |
| 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. 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." |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Mathematical models |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Statistics |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Witten, Daniela |
| Relator term |
author |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Hastie, Trevor |
| Relator term |
author |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Tibshirani, Robert |
| Relator term |
author |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Book |