Optimal decision trees for categorical data via integer programming

Decision trees have been a very popular class of predictive models for decades due to their interpretability and good performance on categorical features. However, they are not always robust and tend to overfit the data. Additionally, if allowed to grow large, they lose interpretability. In this pap...

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Veröffentlicht in:	Journal of global optimization Jg. 81; H. 1; S. 233 - 260
Hauptverfasser:	Günlük, Oktay, Kalagnanam, Jayant, Li, Minhan, Menickelly, Matt, Scheinberg, Katya
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.09.2021 Springer Springer Nature B.V
Schlagworte:	binary classification Combinatorial analysis Computer Science Decision trees Integer programming machine learning Mathematics MATHEMATICS AND COMPUTING Mathematics and Statistics Mixed integer Operations Research/Decision Theory Optimization Prediction models Real Functions Robustness (mathematics) S.i.: Gerad-40 Integer programming Decision trees Binary classification Machine learning
ISSN:	0925-5001, 1573-2916
Online-Zugang:	Volltext
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