The Elements of Statistical Learning: Data Mining, Inference, and PredictionDuring the past decade there has been an explosion in computation and information technology. 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. Many of these tools have common underpinnings but are often expressed with different terminology. 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. Many examples are given, with a liberal use of color 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 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. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. 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 gradient boosting. |
From inside the book
Results 1-5 of 81
Data Mining, Inference, and Prediction Trevor Hastie, Robert Tibshirani, Jerome Friedman. 1 Introduction Statistical ... training set of data , in which we observe the outcome and feature measurements TABLE 1.1 . Average percentage of ...
Data Mining, Inference, and Prediction Trevor Hastie, Robert Tibshirani, Jerome ... set G associated with G. For a two - class G , one approach is to denote the ... training data , with which to construct our predic- tion rule . 2.3 Two ...
... set of training data ? There are many different methods , but by far the most popular is the method of least squares . In this approach , we pick the coefficients ẞ to minimize the residual sum of squares N RSS ( 3 ) = Σ ( yi - x3 ) 2 ...
... set of points in IR2 classified as RED corresponds to { x : xTB > 0.5 } , indicated in Figure 2.1 , and the two predicted classes are separated by the decision ... training data in each class came. Least Squares and Nearest Neighbors 13.
... training data in each class came from a mixture of 10 low- variance Gaussian ... set T clos- est in input space to x to form Ŷ . Specifically , the k ... training sample . Closeness implies a metric , which for the moment we assume is ...
Contents
1 | |
3 | |
5 | |
7 | |
9 | |
11 | |
Bibliographic Notes | 75 |
41 | 108 |
79 | 282 |
Bibliographic Notes | 295 |
Support Vector Machines | 350 |
Bibliographic Notes | 367 |
Flexible Discriminants | 371 |
Bibliographic Notes | 406 |
Prototype Methods and NearestNeighbors | 410 |
Unsupervised Learning | 437 |
55 | 146 |
Bibliographic Notes | 155 |
73 | 159 |
Kernel Methods | 165 |
Additive Models Trees and Related Methods | 257 |
165 | 264 |
Bibliographic Notes | 504 |
81 | 511 |
91 | 517 |
Author Index | 523 |
95 | 530 |