During 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.

INTRODUCTION
Statistical learning plays a key role in many areas of science, finance and industry. Here are some examples of learning problems:

• Predict whether a patient, hospitalized due to a heart attack, will have a second heart attack. The prediction is to be based on demographic, diet and clinical measurements for that patient.
• Predict the price of a stock in 6 months from now, on the basis of company performance measures and economic data.
• Identify the numbers in a handwritten ZIP code, from a digitized image.
• Estimate the amount of glucose in the blood of a diabetic person, from the infrared absorption spectrum of that person's blood.
• Identify the risk factors for prostate cancer, based on clinical and demographic variables.

The science of learning plays a key role in the fields of statistics, data mining and artificial intelligence, intersecting with areas of engineering and other disciplines.

This book is about learning from data. In a typical scenario, we have an outcome measurement, usually quantitative (such as a stock price) or categorical (such as heart attack/no heart attack), that we wish to predict
based on a set of features ...

Visit The Elements of Statistical Learning: Data Mining, Inference, and Prediction Download Page

You can download The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition in PDF format.

Hardcover: 746 pages
Publisher: Springer; 2nd ed. 2009. Corr. 3rd printing edition (February 9, 2009)
Language: English
ISBN-10: 0387848576
ISBN-13: 978-0387848570

ABOUT THE AUTHORS
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.

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