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
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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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Unbiased Recursive Partitioning: A Conditional Inference Framework
Hothorn, T., Hornik, K., & Zeileis, A. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x133933 · Cited by 4,021
An exploratory technique for investigating large quantities of categorical data
Kass, G. V. (1980) · Applied Statistics · doi:10.2307/2986296 · Cited by 2,880
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Fifty Years of Classification and Regression Trees
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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
- OpenAlex
- W2016023958
- Cited by
- 2,285
- References
- 5
- FWCI
- 79.53
- Type
- journal article
- Language
- en
- Open access
- closed
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