An introduction to statistical learning: with applications in R
James, Gareth
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
519.5 / JAM
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
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
519.5 / JAM