An introduction to statistical learning : with applications in R / Statistical learning Gareth James... [et al.]. - New York : Springer, 2017. - xvi, 426 pages : illustrations (some color) ; 24 cm. - Springer texts in statistics, 103 1431-875X ; .

Includes index.

Introduction.- Statistical Learning.- Linear Regression.- Classification.- Resampling Methods.- Linear Model Selection and Regularization.- Moving Beyond Linearity.- Tree-Based Methods.- Support Vector Machines.- Unsupervised Learning.- Index. Table of contents

This book presents key modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering.

9781461471370 1461471370 (acidfree paper)


Mathematical statistics.
Mathematical models.
Mathematical statistics--Problems, exercises, etc.
Mathematical models--Problems, exercises, etc.
R (Computer program language)

QA276 / .I585 2013

519.5 / INT 2017

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