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)
2013936251
Mathematical statistics.
Mathematical models.
Mathematical statistics--Problems, exercises, etc.
Mathematical models--Problems, exercises, etc.
R (Computer program language)
Statistics.
QA276 / .I585 2013
519.5 / INT 2017
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)
2013936251
Mathematical statistics.
Mathematical models.
Mathematical statistics--Problems, exercises, etc.
Mathematical models--Problems, exercises, etc.
R (Computer program language)
Statistics.
QA276 / .I585 2013
519.5 / INT 2017