An introduction to statistical learning : with applications in R /
An introduction to statistical learning : with applications in R /
Statistical learning
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
- xvi, 426 pages : illustrations (some color) ; 24 cm.
- Springer texts in statistics, 103 1431-875X ; .
- Springer texts in statistics ; 103. .
Includes index.
Statistical learning -- Linear regression -- Classification -- resampling methods -- Linear models selection and regularization and regularization -- Moving beyond linearity -- Tree-based methods -- Support Vector machines -- Unsupervised learning.
9781461471370 (acidfree paper) 1461471370 (acidfree paper)
2013936251
Mathematical statistics.
Mathematical models.
Mathematical statistics--Problems, exercises, etc.
Mathematical models--Problems, exercises, etc.
R (Computer program language)
Statistics.
QA276 / .I58 2013
519.5
Includes index.
Statistical learning -- Linear regression -- Classification -- resampling methods -- Linear models selection and regularization and regularization -- Moving beyond linearity -- Tree-based methods -- Support Vector machines -- Unsupervised learning.
9781461471370 (acidfree paper) 1461471370 (acidfree paper)
2013936251
Mathematical statistics.
Mathematical models.
Mathematical statistics--Problems, exercises, etc.
Mathematical models--Problems, exercises, etc.
R (Computer program language)
Statistics.
QA276 / .I58 2013
519.5