A Novel Hyperparameter-free Approach to Decision Tree Construction that Avoids Overfitting by Design

Decision trees are an extremely popular machine learning technique. Unfortunately, overfitting in decision trees still remains an open issue that sometimes prevents achieving good performance. In this work, we present a novel approach for the construction of decision trees that avoids the overfittin...

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Bibliographic Details
Main Authors Leiva, Rafael Garcia, Anta, Antonio Fernandez, Mancuso, Vincenzo, Casari, Paolo
Format Journal Article
LanguageEnglish
Published 04.06.2019
Subjects
Computer Science - Artificial Intelligence
Computer Science - Learning
Statistics - Machine Learning
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Summary:Decision trees are an extremely popular machine learning technique. Unfortunately, overfitting in decision trees still remains an open issue that sometimes prevents achieving good performance. In this work, we present a novel approach for the construction of decision trees that avoids the overfitting by design, without losing accuracy. A distinctive feature of our algorithm is that it requires neither the optimization of any hyperparameters, nor the use of regularization techniques, thus significantly reducing the decision tree training time. Moreover, our algorithm produces much smaller and shallower trees than traditional algorithms, facilitating the interpretability of the resulting models.
DOI:10.48550/arxiv.1906.01246