MARC details
| 000 -LEADER |
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| fixed length control field |
02497nam a2200241Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
NULRC |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250520102814.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250520s9999 xx 000 0 und d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781461471370 |
| 040 ## - CATALOGING SOURCE |
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| Transcribing agency |
NULRC |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA 276 .I58 2013 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
James, Gareth |
| Relator term |
author |
| 245 #3 - TITLE STATEMENT |
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| Title |
An introduction to statistical learning with applications in R / |
| Statement of responsibility, etc. |
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani |
| 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 |
|---|
| Extent |
xiv, 426 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
24 cm. |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
USD44.49 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. 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, 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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
MATHEMATICAL STATISTICS |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Witten, Daniela ;Hastie, Trevor;Tibshirani, Robert |
| Relator term |
co-author;co-author;co-author |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Books |
