The Elements of Statistical Learning
Stanford University statistics professors Trevor Hastie, Robert Tibshirani, and Jerome Friedman They are well-known researchers in this field: Hastie and Tibshirani created generalized additive models and produced a bestselling book with the same name. Hastie invented main curves and surfaces and co-developed most of the statistical modeling tools and environment in R/S-PLUS. Tibshirani invented the lasso and is co-author of the best-selling An Introduction to Bootstrap. Friedman co-invented numerous data-mining technologies, including CART, MARS, projection pursuit, and gradient boosting.
In a shared conceptual framework, The Elements of Statistical Learning presents essential ideas in a number of professions such as medical, biology, finance, and marketing. Despite the statistical approach, the emphasis is on concepts rather than mathematics. Many examples are provided, with extensive use of color visuals. It's an excellent resource for statisticians and anyone else interested in data mining in research or industry. The book covers a wide range of topics, from supervised learning (prediction) to unsupervised learning. Among the several subjects covered are neural networks, support vector machines, classification trees, and boosting, which is the first complete coverage of this topic in any book.
Many areas not included in the original are covered in this important new edition, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. A chapter on approaches for "broad" data (p greater than n), covering multiple testing and false discovery rates, is also included.
Author: Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Link to buy: https://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576/
Ratings: 4.6 out of 5 stars (from 966 reviews)
Best Sellers Rank: #53,971 in Books
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