This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework.
There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates..
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.
The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
The book's coverage is broad, from supervised Learning (prediction) to unsupervised learning.
It is a valuable resource for statisticians and anyone interested in data mining in science or industry.
Many examples are given, with a liberal use of colour graphics.
While the approach is statistical, the emphasis is on concepts rather than mathematics.
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework
