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

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) by Trevor Hastie (Author), Robert Tibshirani (Author), Jerome Friedman (Author). Throughout the past decade there was an explosion in computation and data technology. With it have come huge quantities of information in a wide range of fields resembling medicine, biology, finance, and marketing. The challenge of understanding these information has led to the development of latest tools in the subject of statistics, and spawned new areas akin to information mining, machine studying, and bioinformatics. Many of those tools have widespread underpinnings but are sometimes expressed with different terminology. This guide describes the vital concepts in these areas in a standard conceptual framework. While the strategy is statistical, the emphasis is on concepts somewhat than mathematics. Many examples are given, with a liberal use of coloration graphics. It's a helpful resource for statisticians and anyone all in favour of data mining in science or industry. 

The ebook's protection is broad, from supervised studying (prediction) to unsupervised learning. The numerous subjects include neural networks, help vector machines, classification trees and boosting---the first complete remedy of this topic in any book. This major re-creation features many matters not coated in the unique, including graphical fashions, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-adverse matrix factorization, and spectral clustering. There may be additionally a chapter on strategies for ``wide'' knowledge (pgr eater than n), including multiple testing and false discovery rates. 

I exploit data mining tools in my monetary engineering and monetary modeling work and I've discovered this e book to be very useful. This book provides two crucial sorts of information. First, it supplies enough theory to permit a possible person to grasp the essential insights that encourage specific methods and to evaluate the situations in which those technique are appropriate. Second, the e book gives the exact algorithms to implement the assorted techniques. Whereas no book I've seen covers each knowledge mining methodology obtainable, this one has the strongest coverage I have seen in additive models, non-linear regression, and CART/MART (regression/classification timber). 

It also has very strong coverage in many other areas. I highly advocate it. During the previous decade there was an explosion in computation and knowledge technology. With it have come vast amounts of information in a variety of fields akin to drugs, biology, finance, and marketing. The problem of understanding these knowledge has led to the event of new instruments in the field of statistics, and spawned new areas corresponding to knowledge mining, machine learning, and bioinformatics. Many of those tools have frequent underpinnings but are sometimes expressed with completely different terminology. This e book describes the important ideas in these areas in a typical conceptual framework. Whereas the method is statistical, the emphasis is on ideas relatively than mathematics. Many examples are given, with a liberal use of shade graphics. It is a beneficial resource for statisticians and anyone interested in data mining in science or industry. The guide's coverage is broad, from supervised learning (prediction) to unsupervised learning. The numerous subjects embrace neural networks, help vector machines, classification timber and boosting---the primary comprehensive treatment of this matter in any book. This main new version options many topics not coated within the original, including graphical fashions, random forests, ensemble strategies, least angle regression & path algorithms for the lasso, non-destructive matrix factorization, and spectral clustering. 

There's additionally a chapter on strategies for ``broad'' knowledge (p larger than n), together with a number of testing and false discovery rates. I take advantage of knowledge mining tools in my financial engineering and financial modeling work and I've discovered this book to be very useful. This guide offers two crucial forms of information. First, it gives enough theory to permit a possible user to grasp the important insights that motivate particular techniques and to guage the situations wherein those approach are appropriate. Second, the book gives the precise algorithms to implement the assorted techniques. Whereas no e-book I've seen covers every information mining methodology out there, this one has the strongest coverage I've seen in additive fashions, non-linear regression, and CART/MART (regression/classification bushes). It additionally has very strong coverage in many different areas. I highly advocate it. 

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

 Trevor Hastie (Author), Robert Tibshirani (Author), Jerome Friedman (Author)

 768 pages

Springer; 0002-2009. Corr. 3rd edition (February 9, 2009)

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