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 paper, we present a novel approach for the construction of decision trees that avoids the overfitti...

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Veröffentlicht in:	IEEE access 2019, Vol.7, p.99978-99987
Hauptverfasser:	Garcia Leiva, Rafael, Fernandez Anta, Antonio, Mancuso, Vincenzo, Casari, Paolo
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Complexity theory Computational modeling Decision trees interpretability Kolmogorov complexity Machine learning Optimization Prediction algorithms Regularization Training Vegetation
Online-Zugang:	Volltext
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