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by: Trevor Hastie, Robert Tibshirani, Jerome Friedman

 : The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
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Binding: Hardcover
Brand: Springer
Color: red
EAN: 9780387848570
Edition: 2nd
Feature: This refurbished product is tested and certified to work properly. The product will have minor blemishes and/or light scratches. The refurbishing process includes functionality testing, basic cleaning, inspection, and repackaging. The product ships with all relevant accessories, and may arrive in a generic box.
ISBN: 0387848576
Item Dimensions: 140930305600
Label: Springer
Languages: EnglishPublishedEnglishOriginal LanguageEnglishUnknown
Manufacturer: Springer
MPN: 25645136
Number Of Items: 1
Number Of Pages: 745
Publication Date: 2016
Publisher: Springer
Studio: Springer

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This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketingĀ 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 isĀ 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.





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