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An Introduction to Statistical Learning with Applications in R /

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. Thi...

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Main Authors: James, Gareth. (Author, http://id.loc.gov/vocabulary/relators/aut), Witten, Daniela. (http://id.loc.gov/vocabulary/relators/aut), Hastie, Trevor. (http://id.loc.gov/vocabulary/relators/aut), Tibshirani, Robert. (http://id.loc.gov/vocabulary/relators/aut)
Corporate Author: SpringerLink (Online service)
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2013.
Edition:1st ed. 2013.
Series:Springer Texts in Statistics, 103
Subjects:
Statistics .
Artificial intelligence.
Statistical Theory and Methods.
Statistics and Computing/Statistics Programs.
Artificial Intelligence.
Statistics, general.
Online Access:https://doi.org/10.1007/978-1-4614-7138-7
Table of Contents:
  • 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.

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