9781071614174 - An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) von James, Gareth; Witten, Daniela; Hastie, Trevor; Tibshirani, Rober... (12 Ergebnisse)

hardcover. Zustand: Fine.

hardcover. Zustand: Very Good.

Zustand: New. Presents an essential statistical learning toolkit for practitioners in science, industry, and other fieldsDemonstrates application of the statistical learning methods in RIncludes new chapters on deep learning, survival analysis, and multi.

Zustand: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1100grams, ISBN:9781071614174.

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1100grams, ISBN:9781071614174.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Buch. Zustand: Neu. Neuware -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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 607 pp. Englisch.

Buch. Zustand: Neu. Neuware - 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.

Hardcover. Zustand: Brand New. 2nd edition. 622 pages. 9.25x6.10x1.22 inches. In Stock.

Zustand: New.

Zustand: New. 2021. 2nd ed. 2021. Hardcover. . . . . . Books ship from the US and Ireland.

Hardcover. Zustand: Brand New. 2nd edition. 622 pages. 9.25x6.10x1.22 inches. In Stock.