An introduction to statistical learning: with applications in R
- 2nd
- New York Springer 2021
- xv, 607 p.
Table of content
Front Matter Pages i-xv PDF Introduction Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 1-14 Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 15-57 Linear Regression Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 59-128 Classification Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 129-195 Resampling Methods Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 197-223 Linear Model Selection and Regularization Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 225-288 Moving Beyond Linearity Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 289-326 Tree-Based Methods Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 327-365 Support Vector Machines Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 367-402 Deep Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 403-460 Survival Analysis and Censored Data Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 461-495 Unsupervised Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 497-552 Multiple Testing Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 553-595
Presents an essential statistical learning toolkit for practitioners in science, industry, and other fields Demonstrates application of the statistical learning methods in R Includes new chapters on deep learning, survival analysis, and multiple testing Covers a range of topics, such as linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and deep learning Features extensive color graphics for a dynamic learning experience
9781071614174
Mathematical statistics R (Computer program language) Mathematical models