An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

*An Introduction to Statistical Learning* offers an accessible overview of statistical learning, a critical toolkit for interpreting complex datasets across diverse fields such as biology, finance, marketing, and astrophysics. The book focuses on essential modeling and prediction techniques, illustrating them with color graphics and real-world examples to aid understanding. Key topics covered include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering. This second edition expands the curriculum with new chapters on deep learning, survival analysis, and multiple testing, alongside updated treatments of naïve Bayes and generalized linear models. Designed to facilitate practical application, the text targets both statisticians and non-statisticians who wish to implement cutting-edge techniques in their work. Each chapter includes a tutorial on using R, a popular open-source statistical software, to perform the analyses discussed. The authors, two of whom co-wrote the advanced reference *The Elements of Statistical Learning*, have tailored this volume to a broader audience by assuming only a prior course in linear regression and avoiding matrix algebra. By balancing theoretical concepts with practical R code implementation, the book serves as a comprehensive resource for practitioners in science and industry seeking to analyze data effectively.