The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

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ISBN-13: 9780387848570
ISBN-10: 0387848576
Edition: 2nd
Author: Trevor Hastie, Robert Tibshirani, Jerome Friedman
Publication date: 2016
Publisher: Springer
Format: Hardcover 767 pages
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Book details

ISBN-13: 9780387848570
ISBN-10: 0387848576
Edition: 2nd
Author: Trevor Hastie, Robert Tibshirani, Jerome Friedman
Publication date: 2016
Publisher: Springer
Format: Hardcover 767 pages

Summary

Acknowledged authors Trevor Hastie , Robert Tibshirani , Jerome Friedman wrote The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) comprising 767 pages back in 2016. Textbook and eTextbook are published under ISBN 0387848576 and 9780387848570. Since then The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) textbook received total rating of 4 stars and was available to sell back to BooksRun online for the top buyback price of $ 20.97 or rent at the marketplace.

Description

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.

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