The Elements of Statistical Learning



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.

10 reviews for The Elements of Statistical Learning

  1. Aran Canes

    Math books, at least data science texts, can usually be divided into those which are easy to read but contain little technical rigor and those which are written with a scientific approach to methodology but are so equation dense that it’s hard to imagine them being read outside an advanced academic setting.

    Fortunately, The Elements of Statistical Learning proves the exception. The text is full with the equations necessary to root the methodology without engaging the reader with long proofs that would tax those of us employing these techniques in the business world.

    The visual aspects of the text seem to have been written with John Tukey or Edward Tufte in mind. Though their frequent use makes the book some seven hundred pages long, reading and comprehension is made much easier.

    And, though it’s been almost ten years since the book was published, the techniques described remain, for the most part, at the cutting edge of data science.

    I was told by some other analysts I know that this was their bible for data science. I was somewhat skeptical of this kind of hyperbole but was pleasantly surprised that the book matched these high expectations. If you have an undergraduate degree in a mathematically related discipline, The Elements of Statistical Learning will prove to be an invaluable reference to understand the rapidly advancing avalanche of data mining techniques.

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