Decision Tree Pruning
Decision Tree Pruning is a model simplification technique that belongs to the class of regularization methods in machine learning, specifically distinguished by its removal of branches or subtrees from a decision tree to reduce overfitting by improving generalization performance on unseen categorical data.
333
Publications
25.1K
Citations
845
Authors
416
Institutions
Publications per year
2017–2026
85
333
SPLIT SELECTION METHODS FOR CLASSIFICATION TREES
Wei‐Yin Loh, Yu‐Shan Shih · 1997 · 943 citations
A System for Induction of Oblique Decision Trees
1994 · 716 citations
1–5 of 333
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Laboratoire d'Ingénierie des Systèmes Complexes, France
3 publications · 35 citations
3
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3
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1–5 of 845
416
University of Wisconsin–Madison
Madison, United States
5 h-index
8 publications
1.2K citations
Stanford University
Stanford, United States
5 h-index
10 publications
1.4K citations
AT&T (United States)
Bedminster, United States
5 h-index
7 publications
665 citations
Purdue University West Lafayette
West Lafayette, United States
4 h-index
7 publications
401 citations
University of California, Santa Cruz
Santa Cruz, United States
4 h-index
4 publications
244 citations
Yale University
New Haven, United States
3 h-index
3 publications
415 citations

Polish Academy of Sciences
Warsaw, Poland
3 h-index
3 publications
267 citations
University College London
London, United Kingdom
3 h-index
8 publications
2.9K citations
1–8 of 416
8 publications · 517 citations
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5 publications · 326 citations
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Repository · 5 publications · 108 citations
5
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JAIR · 4 publications · 3.4K citations
4
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IDA · 4 publications · 181 citations
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