An Introduction to Statistical Learning: With Applications in R

Valutazione media 4,62
( su 472 valutazioni fornite da Goodreads )
 
9781461471370: An Introduction to Statistical Learning: With Applications in R

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. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Recensione:

“This book by James, Witten, Hastie, and Tibshirani was a great pleasure to read, and I was extremely surprised by it and the available material. In my opinion, it is the best book for teaching statistical learning approaches to undergraduate and master students in statistics. ... All in all, this is a great textbook for teaching an introductory course in statistical learning. ... In my opinion, there is no better book for teaching modern statistical learning at the introductory level.” (Andreas Ziegler, Biometrical Journal, Vol. 58 (3), May, 2016)

“This book has a very strong advantage that sets it well ahead of the competition when it comes to learning about machine learning: it covers all of the necessary details that one has to know in order to apply or implement a machine learning algorithm in a real-world problem. Hence, this book will definitely be of interest to readers from many fields, ranging from computer science to business administration and marketing.” (Charalambos Poullis, Computing Reviews, September, 2014)

“The book provides a good introduction to R. The code for all the statistical methods introduced in the book is carefully explained. ... the book will certainly be useful to many people (including me). I will surely use many examples, labs and datasets from this book in my own lectures.” (Pierre Alquier, Mathematical Reviews, July, 2014)

“The stated purpose of this book is to facilitate the transition of statistical learning to mainstream. ... it adds information by including more detail and R code to some of the topics in Elements of Statistical Learning. ... I am having a lot of fun playing with the code that goes with book. I am glad that this was written.” (Mary Anne, Cats and Dogs with Data, maryannedata.com, June, 2014)

“This book (ISL) is a great Master’s level introduction to statistical learning: statistics for complex datasets. ... the homework problems in ISL are at a Master’s level for students who want to learn how to use statistical learning methods to analyze data. ... ISL contains 12 very valuable R labs that show how to use many of the statistical learning methods with the R package ISLR ... .” (David Olive, Technometrics, Vol. 56 (2), May, 2014)

“Written by four experts of the field, this book offers an excellent entry to statistical learning to a broad audience, including those without strong background in mathematics. ... The end-of-chapter exercises make the book an ideal text for both classroom learning and self-study. ... The book is suitable for anyone interested in using statistical learning tools to analyze data. It can be used as a textbook for advanced undergraduate and master’s students in statistics or related quantitative fields.” (Jianhua Z. Huang, Journal of Agricultural, Biological, and Environmental Statistics, Vol. 19, 2014)

“It aims to introduce modern statistical learning methods to students, researchers and practitioners who are primarily interested in analysing data and want to be confined only with the implementation of the statistical methodology and subsequent interpretation of the results. ... the book also demonstrates how to apply these methods using various R packages by providing detailed worked examples using interesting real data applications.” (Klaus Nordhausen, International Statistical Review, Vol. 82 (1), 2014)

“The book is structured in ten chapters covering tools for modeling and mining of complex real life data sets. ... The style is suitable for undergraduates and researchers ... and the understanding of concepts is facilitated by the exercises, both practical and theoretical, which accompany every chapter.” (Irina Ioana Mohorianu, zbMATH, Vol. 1281, 2014) 

"The book excels in providing the theoretical and mathematical basis for machine learning, and now at long last, a practical view with the inclusion of R programming examples. It is the latter portion of the update that I’ve been waiting for as it directly applies to my work in data science. Give the new state of this book, I’d classify it as the authoritative text for any machine learning practitioner...This is one book you need to get if you’re serious about this growing field." (Daniel Gutierrez, Inside Big Data, inside-bigdata.com, October 2013)

L'autore:

Gareth James is a professor of statistics at University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area. 

Daniela Witten is an assistant professor of biostatistics at University of Washington. Her research focuses largely on high-dimensional statistical machine learning. She has contributed to the translation of statistical learning techniques to the field of genomics, through collaborations and as a member of the Institute of Medicine committee that led to the report Evolution of Translational Omics.


Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

I migliori risultati di ricerca su AbeBooks

1.

Gareth James; Daniela Witten; Trevor Hastie; Robert Tibshirani
ISBN 10: 1461471370 ISBN 13: 9781461471370
Nuovi Quantità: 1
Da
BWB
(Valley Stream, NY, U.S.A.)
Valutazione libreria
[?]

Descrizione libro Condizione libro: New. Depending on your location, this item may ship from the US or UK. Codice libro della libreria 97814614713700000000

Maggiori informazioni su questa libreria | Fare una domanda alla libreria

Compra nuovo
EUR 48,36
Convertire valuta

Aggiungere al carrello

Spese di spedizione: GRATIS
In U.S.A.
Destinazione, tempi e costi

2.

Gareth James
ISBN 10: 1461471370 ISBN 13: 9781461471370
Nuovi Rilegato Quantità: > 20
Da
GREAT BOOKS DEAL
(TALLAHASSEE, FL, U.S.A.)
Valutazione libreria
[?]

Descrizione libro Hardcover. Condizione libro: New. Brand NEW. Standard delivery takes 3-6 business days by USPS/UPS/Fedex with tracking number. Choose expedited shipping for superfast delivery 2-4 business days. We also ship to PO Box addresses. 100% Customer satisfaction guaranteed! Please feel free to contact us for any queries. Codice libro della libreria ORC-1119

Maggiori informazioni su questa libreria | Fare una domanda alla libreria

Compra nuovo
EUR 46,33
Convertire valuta

Aggiungere al carrello

Spese di spedizione: EUR 3,44
In U.S.A.
Destinazione, tempi e costi

3.

JAMES
ISBN 10: 1461471370 ISBN 13: 9781461471370
Nuovi Quantità: 2
Da
firstbookstore
(New Delhi, India)
Valutazione libreria
[?]

Descrizione libro Condizione libro: Brand New. Brand New Original US Edition, Perfect Condition. Printed in English. Excellent Quality, Service and customer satisfaction guaranteed!. Codice libro della libreria AIND-91596

Maggiori informazioni su questa libreria | Fare una domanda alla libreria

Compra nuovo
EUR 50,22
Convertire valuta

Aggiungere al carrello

Spese di spedizione: GRATIS
Da: India a: U.S.A.
Destinazione, tempi e costi

4.

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Editore: Springer New York 2013-06-25, New York (2013)
ISBN 10: 1461471370 ISBN 13: 9781461471370
Nuovi Rilegato Quantità: > 20
Da
Blackwell's
(Oxford, OX, Regno Unito)
Valutazione libreria
[?]

Descrizione libro Springer New York 2013-06-25, New York, 2013. hardback. Condizione libro: New. Codice libro della libreria 9781461471370

Maggiori informazioni su questa libreria | Fare una domanda alla libreria

Compra nuovo
EUR 47,72
Convertire valuta

Aggiungere al carrello

Spese di spedizione: EUR 3,39
Da: Regno Unito a: U.S.A.
Destinazione, tempi e costi

5.

JAMES
ISBN 10: 1461471370 ISBN 13: 9781461471370
Nuovi Quantità: 2
Da
Romtrade Corp.
(STERLING HEIGHTS, MI, U.S.A.)
Valutazione libreria
[?]

Descrizione libro Condizione libro: New. Brand New Original US Edition.We Ship to PO BOX Address also. EXPEDITED shipping option also available for faster delivery. Codice libro della libreria AUSBNEW-91596

Maggiori informazioni su questa libreria | Fare una domanda alla libreria

Compra nuovo
EUR 53,42
Convertire valuta

Aggiungere al carrello

Spese di spedizione: GRATIS
In U.S.A.
Destinazione, tempi e costi

6.

Gareth James, Trevor Hastie, Robert Tibshirani
Editore: Springer-Verlag New York Inc., United States (2016)
ISBN 10: 1461471370 ISBN 13: 9781461471370
Nuovi Rilegato Quantità: 10
Da
The Book Depository US
(London, Regno Unito)
Valutazione libreria
[?]

Descrizione libro Springer-Verlag New York Inc., United States, 2016. Hardback. Condizione libro: New. Corr. 6th printing. 239 x 157 mm. Language: English . Brand New Book. 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. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. Codice libro della libreria SPR9781461471370

Maggiori informazioni su questa libreria | Fare una domanda alla libreria

Compra nuovo
EUR 57,15
Convertire valuta

Aggiungere al carrello

Spese di spedizione: GRATIS
Da: Regno Unito a: U.S.A.
Destinazione, tempi e costi

7.

Gareth James, Trevor Hastie, Robert Tibshirani
Editore: Springer-Verlag New York Inc., United States (2016)
ISBN 10: 1461471370 ISBN 13: 9781461471370
Nuovi Rilegato Quantità: 10
Da
The Book Depository
(London, Regno Unito)
Valutazione libreria
[?]

Descrizione libro Springer-Verlag New York Inc., United States, 2016. Hardback. Condizione libro: New. Corr. 6th printing. 239 x 157 mm. Language: English . Brand New Book. 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. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. Codice libro della libreria SPR9781461471370

Maggiori informazioni su questa libreria | Fare una domanda alla libreria

Compra nuovo
EUR 57,82
Convertire valuta

Aggiungere al carrello

Spese di spedizione: GRATIS
Da: Regno Unito a: U.S.A.
Destinazione, tempi e costi

8.

James, Gareth; Hastie, Trevor; Tibshirani, Robert; Witten, Daniela
Editore: Springer-Verlag New York Inc. (2013)
ISBN 10: 1461471370 ISBN 13: 9781461471370
Nuovi Rilegato Prima edizione Quantità: > 20
Valutazione libreria
[?]

Descrizione libro Springer-Verlag New York Inc., 2013. Condizione libro: New. This book presents key modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering. Series: Springer Texts in Statistics. Num Pages: 426 pages, 150 black & white illustrations, 10 black & white tables, biography. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 241 x 163 x 25. Weight in Grams: 870. . 2013. 1st Edition. Hardcover. . . . . . Codice libro della libreria V9781461471370

Maggiori informazioni su questa libreria | Fare una domanda alla libreria

Compra nuovo
EUR 58,29
Convertire valuta

Aggiungere al carrello

Spese di spedizione: GRATIS
Da: Irlanda a: U.S.A.
Destinazione, tempi e costi

9.

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Editore: Springer 2013-08-12 (2013)
ISBN 10: 1461471370 ISBN 13: 9781461471370
Nuovi Rilegato Quantità: 5
Da
Chiron Media
(Wallingford, Regno Unito)
Valutazione libreria
[?]

Descrizione libro Springer 2013-08-12, 2013. Hardcover. Condizione libro: New. Codice libro della libreria NU-LBR-01214977

Maggiori informazioni su questa libreria | Fare una domanda alla libreria

Compra nuovo
EUR 55,39
Convertire valuta

Aggiungere al carrello

Spese di spedizione: EUR 3,38
Da: Regno Unito a: U.S.A.
Destinazione, tempi e costi

10.

Gareth James, Trevor Hastie, Robert Tibshirani, Daniela Witten
Editore: Springer-Verlag New York Inc.
ISBN 10: 1461471370 ISBN 13: 9781461471370
Nuovi Rilegato Quantità: 7
Da
THE SAINT BOOKSTORE
(Southport, Regno Unito)
Valutazione libreria
[?]

Descrizione libro Springer-Verlag New York Inc. Hardback. Condizione libro: new. BRAND NEW, An Introduction to Statistical Learning: With Applications in R (1st ed. 2013, Corr. 5th printing 2015), Gareth James, Trevor Hastie, Robert Tibshirani, Daniela Witten, 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. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. Codice libro della libreria B9781461471370

Maggiori informazioni su questa libreria | Fare una domanda alla libreria

Compra nuovo
EUR 50,93
Convertire valuta

Aggiungere al carrello

Spese di spedizione: EUR 7,84
Da: Regno Unito a: U.S.A.
Destinazione, tempi e costi

Vedi altre copie di questo libro

Vedi tutti i risultati per questo libro