Read The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) by Trevor Hastie, Robert Tibshirani, Jerome Friedman Online
* The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) ✓ PDF Download by # Trevor Hastie, Robert Tibshirani, Jerome Friedman eBook or Kindle ePUB Online free. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. During the past decade there has been an explosion in computation and information technology. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery
| Title | : | The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) |
| Author | : | |
| Rating | : | 4.41 (619 Votes) |
| Asin | : | 0387848576 |
| Format Type | : | paperback |
| Number of Pages | : | 745 Pages |
| Publish Date | : | 2014-07-19 |
| Language | : | English |
From the reviews:"Like the first edition, the current one is a welcome edition to researchers and academicians equally…. 51, NO. The book is so comprehensive that it offers material for several courses." (Klaus Nordhausen, International Statistical Review, Vol. Almost all of the chapters are revised.… The Material is nicely reorganized and repackaged, with the general layout being the same as that of the first edition.… If you bought the first edition, I suggest that you buy the second editon for maximum effect, and if you haven’t, then I still strongly recommend you have this book at your desk. 77 (3),
Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and
John Mount said Actually does something (huge) with the math. I have been using The Elements of Statistical Learning for years, so it is finally time to try and review it.The Elements of Statistical Learning is a comprehensive mathematical treatment of machine learning from a statistical perspective. This means you get good derivations of popular methods such as support vector machines, random forests, and graphical models; but each is developed only after the appropriate (and wrongly considered less sexy) statistical framework has already been derived (linea. my big brown book of statistic learning tools This is a quite interesting, and extremely useful book, but it is wearing to read in large chunks. The problem, if you want to call it that, is that it is essentially a 700 page catalogue of clever hacks in statistical learning. From a technical point of view it is well-ehough structured, but there is not the slightest trace of an overarching philosophy. And if you don't actually have a philosophical perspective in place before you start, the read you face might well be an even harder grind. Be war. Five Stars great book.
This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. During the past decade there has been an explosion in computation and information technology. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.. With it have come vast amounts of data in a
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