ISBN: 9781461471370

Product Edition:0th Edition

Author: Robert Tibshirani, Daniela Witten, Trevor Hastie, Gareth James

Book Name: An Introduction to Statistical Learning

Subject Name: Maths

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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)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.� �The Elements of Statistical Learning: Data Mining Inference and Prediction Second Edition (Springer Series in Statistics)This book describes the important ideas in a variety of fields such as medicine biology finance and marketing�in a common conceptual framework. While the approach is statistical the emphasis is on concepts rather than mathematics. Many examples are given with a liberal use of colour graphics. It is�a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad from supervised learning (prediction) to unsupervised learning. The many topics include neural networks support vector machines classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original including graphical models random forests ensemble methods least angle regression & path algorithms for the lasso non-negative matrix factorisation and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n) including multiple testing and false discovery rates.Read more

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