Classification and Regression Trees
2022
506 citations
Abstract
Classification and regression tree models (CARTs) are computationally intensive methods that are used in situations where there are many explanatory variables and user would like guidance about, possibly, including them in the model: classification trees are where the outcome is discrete and regression trees where the outcome is continuous. A CART model is fitted using binary recursive partitioning, whereby the data are successively split along co-ordinate axes of each of the explanatory variables so that, at any node, the split which maximally distinguishes the response variable in the left and the right branches is selected. Classification trees, for discrete outcomes, are derived using the same R functions as for regression trees.
Keywords
CartRegressionRecursive partitioningMathematicsStatisticsOutcome (game theory)Regression analysisTree (set theory)Decision treeVariable (mathematics)Regression diagnosticLinear regressionArtificial intelligenceComputer scienceCombinatoricsPolynomial regressionGeography
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- Year
- 2022
- Type
- other
- Pages
- 761-777
- Citations
- 506
- Access
- Closed
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Cite This
Elinor Jones,
Simon Harden,
Michael J. Crawley
(2022).
Classification and Regression Trees.
, 761-777.
https://doi.org/10.1002/9781119634461.ch20
Identifiers
- DOI
- 10.1002/9781119634461.ch20