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Tree-Based Methods

  • Gareth James
  • Daniela Witten
  • Trevor Hastie
  • Robert Tibshirani
Chapter
Part of the Springer Texts in Statistics book series (STS, volume 103)

Abstract

In this chapter, we describe tree-based methods for regression and classification. These involve stratifying or segmenting the predictor space into a number of simple regions. In order to make a prediction for a given observation, we typically use the mean or the mode of the training observations in the region to which it belongs. Since the set of splitting rules used to segment the predictor space can be summarized in a tree, these types of approaches are known as decision tree methods.

Keywords

Random Forest Regression Tree Classification Tree Terminal Node Gini Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Gareth James
    • 1
  • Daniela Witten
    • 2
  • Trevor Hastie
    • 3
  • Robert Tibshirani
    • 3
  1. 1.Department of Information and Operations ManagementUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Department of BiostatisticsUniversity of WashingtonSeattleUSA
  3. 3.Department of StatisticsStanford UniversityStanfordUSA

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