Buy **The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition PDF ebook***,* (ISBN-13: **9780387848570**), copyright by author **Trevor Hastie; Robert Tibshirani; Jerome Friedman** – published by Springer today and save up to 80% compared to the print version of this textbook. With PDF version of this textbook, not only save you money, you can also highlight, add text, underline add post-it notes, bookmarks to pages, instantly search for the major terms or chapter titles, etc.

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### Summary of The Elements of Statistical Learning: Data Mining, Inference, and Prediction – 2nd Edition

**From publisher Springer:**

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.

Additional ISBNs: 9780387848570

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