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The DOI matches its Crossref record; 161 of the 161 references it cites were found in a registry or an index.

Fifty Years of Classification and Regression Trees

Loh, Wei‐Yin·2014·International Statistical Review
DOI 10.1111/insr.12016
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Abstract

Fifty years have passed since the publication of the first regression tree algorithm. New techniques have added capabilities that far surpass those of the early methods. Modern classification trees can partition the data with linear splits on subsets of variables and fit nearest neighbor, kernel density, and other models in the partitions. Regression trees can fit almost every kind of traditional statistical model, including least‐squares, quantile, logistic, Poisson, and proportional hazards models, as well as models for longitudinal and multiresponse data. Greater availability and affordability of software (much of which is free) have played a significant role in helping the techniques gain acceptance and popularity in the broader scientific community. This article surveys the developments and briefly reviews the key ideas behind some of the major algorithms.

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Research works printed but not linked: 37

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Metadata

Authors
Loh, W.
Venue
International Statistical Review
Publisher
Wiley
Published
2014-06-30
Volume
82
Issue
3
Pages
329-348
ISSN
  • 0306-7734 (ISSN-L)
  • 1751-5823
DOI
10.1111/insr.12016
OpenAlex
W1519043595
Cited by
649
References
161
FWCI
49.93
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)

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Loh, W. (2014). Fifty Years of Classification and Regression Trees. International Statistical Review. https://doi.org/10.1111/insr.12016

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