James Gareth
An introduction to statistical learning : with applications in R - New York: Springer, [c2013] - 426 p - Springer Texts in Statistics .
Ch 1. Introduction
Ch 2. Statistical Learning
Ch 3. Linear Regression
Ch 4. Classification
Ch 5. Resampling Methods
Ch 6. Linear Model Selection and Regularization
Ch 7. Moving Beyond Linearity
Ch 8. Tree-Based Methods
Ch 9. Support Vector Machines
Ch 10. Unsupervised Learning
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. --- summary provided by publisher
9781461471370
Statistics
QA 276
An introduction to statistical learning : with applications in R - New York: Springer, [c2013] - 426 p - Springer Texts in Statistics .
Ch 1. Introduction
Ch 2. Statistical Learning
Ch 3. Linear Regression
Ch 4. Classification
Ch 5. Resampling Methods
Ch 6. Linear Model Selection and Regularization
Ch 7. Moving Beyond Linearity
Ch 8. Tree-Based Methods
Ch 9. Support Vector Machines
Ch 10. Unsupervised Learning
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. --- summary provided by publisher
9781461471370
Statistics
QA 276