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An introduction to statistical learning : with applications in R
An introduction to statistical learning : with applications in R
- 자료유형
- 단행본
- 160411234444
- ISBN
- 9781461471370 : \111340
- DDC
- 519.5-21
- 청구기호
- 519 J27a
- 저자명
- James, Gareth
- 서명/저자
- An introduction to statistical learning : with applications in R / by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
- 발행사항
- New York : Springer, 2013
- 형태사항
- xvi, 426 p : ill. (some color) ; 24cm
- 총서명
- Springer texts in statistics = 1431-875X ; 103
- 주기사항
- 교수 신청도서
- 주기사항
- 이준표 교수 신청도서, 2016
- 주기사항
- 예술과 과학
- 주기사항
- Art & Technology
- 서지주기
- Includes index
- 초록/해제
- 초록: 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.
- 일반주제명
- Mathematical statistics
- 일반주제명
- Mathematical models
- 일반주제명
- Statistics
- 키워드
- 통계
- 기타저자
- Witten, Daniela
- 기타저자
- Hastie, Trevor
- 기타저자
- Tibshirani, Robert
- 가격
- \100206
- Control Number
- sacl:102708
- 책소개
-
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.
