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An Introduction to Statistical Learning - with Applications in R (Paperback, 2nd ed. 2021)
Loot Price: R1,563
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An Introduction to Statistical Learning - with Applications in R (Paperback, 2nd ed. 2021)
Series: Springer Texts in Statistics
Expected to ship within 12 - 17 working days
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An Introduction to Statistical Learning provides an accessible
overview of the field of statistical learning, an essential toolset
for making sense of the vast and complex data sets that have
emerged in fields ranging from biology to finance to marketing to
astrophysics in the past twenty years. This book presents some of
the most important modeling and prediction techniques, along with
relevant applications. Topics include linear regression,
classification, resampling methods, shrinkage approaches,
tree-based methods, support vector machines, clustering, deep
learning, survival analysis, multiple testing, and more. Color
graphics and real-world examples are used to illustrate the methods
presented. Since the goal of this textbook is to facilitate the use
of these statistical learning techniques by practitioners in
science, industry, and other fields, each chapter contains a
tutorial on implementing the analyses and methods presented in R,
an extremely popular open source statistical software platform. Two
of the authors co-wrote The Elements of Statistical Learning
(Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular
reference book for statistics and machine learning researchers. An
Introduction to Statistical Learning covers many of the same
topics, but at a level accessible to a much broader audience. This
book is targeted at statisticians and non-statisticians alike who
wish to use cutting-edge statistical learning techniques to analyze
their data. The text assumes only a previous course in linear
regression and no knowledge of matrix algebra. This Second Edition
features new chapters on deep learning, survival analysis, and
multiple testing, as well as expanded treatments of naive Bayes,
generalized linear models, Bayesian additive regression trees, and
matrix completion. R code has been updated throughout to ensure
compatibility.
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