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. \\ store use: loc - desk/o:dup
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All returns accepted: ReturnsNotAccepted
Number of Pages: Xxii, 745 Pages
Language: English
Publication Name: Elements of Statistical Learning : Data Mining, Inference, and Prediction
Publisher: Springer New York
Publication Year: 2017
Subject: Probability & Statistics / General, Intelligence (Ai) & Semantics, Databases / Data Mining
Item Height: 1.5 in
Item Weight: 51.2 Oz
Type: Textbook
Subject Area: Computers, Mathematics
Author: Trevor Hastie, Jerome Friedman, Robert Tibshirani, J. H. Friedman
Item Length: 9.4 in
Series: Springer Series in Statistics Ser.
Item Width: 6.5 in
Format: Hardcover
