An Introduction to Statistical Learning offers a clear and approachable introduction to the field of statistical learning—a vital toolkit for analyzing the large and complex data sets increasingly common across disciplines such as biology, finance, marketing, and astrophysics. The book covers key modeling and prediction techniques, including linear regression, classification, resampling methods, shrinkage approaches, tree-based models, support vector machines, clustering, deep learning, survival analysis, and multiple testing.
Designed to be practical and accessible, this text uses color graphics and real-world examples to demonstrate how each method works in practice. Each chapter includes step-by-step tutorials using R, a widely-used open-source statistical software, helping readers apply the techniques directly to their own data.
Authored by two contributors to the widely acclaimed The Elements of Statistical Learning, this book presents similar topics in a more accessible format, making it suitable for a broader audience. It is ideal for both statisticians and professionals from other fields looking to adopt modern statistical learning methods. The only prerequisite is a basic understanding of linear regression; no background in matrix algebra is required.
The Second Edition introduces new chapters on deep learning, survival analysis, and multiple testing, while expanding discussions on topics like nave Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. All R code has been updated for current compatibility.
