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

García, Rafael and Fernández Anta, Antonio and Mancuso, Vincenzo and Casari, Paolo (2019) A Novel Hyperparameter-free Approach to Decision Tree Construction that Avoids Overfitting by Design. [Journal Articles] (Accepted for publication)

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Abstract

Decision trees are an extremely popular machine learning technique. Unfortunately, over- fitting 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.

Item Type: Journal Articles
Uncontrolled Keywords: Decision Trees, Regularization, Interpretability, Kolmogorov Complexity.
Subjects: UNSPECIFIED
Divisions: UNSPECIFIED
Depositing User: Vincenzo Mancuso
Date Deposited: 15 Jul 2019 09:33
Last Modified: 18 Jul 2019 06:56
URI: http://eprints.networks.imdea.org/id/eprint/2023

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