Read The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics) by Trevor Hastie, Robert Tibshirani, Jerome Friedman Online
Read [Trevor Hastie, Robert Tibshirani, Jerome Friedman Book] * The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics) Online ! PDF eBook or Kindle ePUB free. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics) Five Stars Jin Jin great. Useful book on data mining I use data mining tools in my financial engineering and financial modeling work and I have found this book to be very useful. This book provides two crucial types of information. First, it provides enough theory to allow a potential user to understand the essential insights that motivate specific techniques and to evaluate the situations in which those technique are appropriate. Second, the book gives the exact algorithms to implement the vari
| Title | : | The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics) |
| Author | : | |
| Rating | : | 4.41 (659 Votes) |
| Asin | : | 0387952845 |
| Format Type | : | paperback |
| Number of Pages | : | 552 Pages |
| Publish Date | : | 2015-07-02 |
| Language | : | English |
… The book can be used as a basis for courses of different levels, from the purely practical to the thoroughly theoretical. 74 (4), 2004)"One of the great features of the book is that it really contains more or less all modern methods for statistical learning, so it gives the reader a very good overview of this important field. … We have taught a large graduate course (for statisticians and computer scientists) in data mining from this book. Furthermore, the practical development of these modeling and inferential tools has resulted in a deeper theoretical understanding of the modeling process The book includes many special cases and examples, which give insights into the ideas and methods. … The style of this beautifully presented book is friendly and intuitive, and at the same time clear and rigorous. covers a wider number of topics such as supervised learning based on linear models, near
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 factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. While the approach is statistical, the emphasis is on concepts rather than mathematics. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Tibshiran
Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popula
Five Stars Jin Jin great. Useful book on data mining I use data mining tools in my financial engineering and financial modeling work and I have found this book to be very useful. This book provides two crucial types of information. First, it provides enough theory to allow a potential user to understand the essential insights that motivate specific techniques and to evaluate the situations in which those technique are appropriate. Second, the book gives the exact algorithms to implement the various techniques.While no book I have seen covers every data mining methodology availa. "Excellent introduction to statistical learning" according to Customer. This book is an excellent survey of the huge area of statistics / computer science called statistical learning. The discussion is interesting and accurate, but not too theoretical. It is the best book to date for a general audience with a reasonable math/stat background. One of the strengths is the wide variety of topics covered; it is very comprehensive. If there is a weakness, it is that depth is limited. Plenty of references are provided for further study, and the authors maintain a website. Recommended as a reference or a
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