Statistical Learning with R Applications

Statistical learning is a vital toolset for analyzing the vast and complex data sets that have emerged in various fields over the past twenty years, including biology, finance, marketing, and astrophysics. An Introduction to Statistical Learning offers an accessible overview of this field, presenting important modeling and prediction techniques such as linear regression, classification, resampling methods, and tree-based methods. The book also covers topics like support vector machines, clustering, deep learning, survival analysis, and multiple testing.

Real-world examples and color graphics are used to illustrate the methods discussed in the book, making it easier for practitioners in science, industry, and other fields to understand and apply these techniques. Each chapter includes a tutorial on implementing the analyses and methods using R, a popular open-source statistical software platform.

Written by authors who also authored The Elements of Statistical Learning, this book is aimed at a wider audience, including statisticians and non-statisticians who want to use advanced statistical learning techniques in their data analysis. No prior knowledge of matrix algebra is required, just a basic understanding of linear regression.

The new Second Edition includes additional chapters on deep learning, survival analysis, and multiple testing, as well as expanded discussions on topics like naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. The R code provided in the book has been updated for compatibility. Overall, An Introduction to Statistical Learning is an essential resource for anyone looking to harness the power of statistical learning techniques in their data analysis.

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