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Cited by 2,285

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Classification and regression trees

Loh, Wei‐Yin·2011·WIREs Data Mining and Knowledge Discovery
DOI 10.1002/widm.8
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Abstract

Classification and regression trees are machine‐learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14‐23 DOI: 10.1002/widm.8 This article is categorized under: Technologies > Classification Technologies > Machine Learning Technologies > Prediction Algorithmic Development > Statistics

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Metadata

Authors
Loh, W.
Venue
WIREs Data Mining and Knowledge Discovery
Publisher
Wiley
Published
2011-01
Volume
1
Issue
1
Pages
14-23
ISSN
  • 1942-4787 (ISSN-L)
  • 1942-4795
DOI
10.1002/widm.8
OpenAlex
W2016023958
Cited by
2,285
References
5
FWCI
79.53
Type
journal article
Language
en
Open access
closed

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