A Novel Hyperparameter-free Approach to Decision Tree Construction that Avoids Overfitting by Design
Machine Learning
2019-06-05 v1 Artificial Intelligence
Machine Learning
Abstract
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.
Cite
@article{arxiv.1906.01246,
title = {A Novel Hyperparameter-free Approach to Decision Tree Construction that Avoids Overfitting by Design},
author = {Rafael Garcia Leiva and Antonio Fernandez Anta and Vincenzo Mancuso and Paolo Casari},
journal= {arXiv preprint arXiv:1906.01246},
year = {2019}
}
Comments
Submitted to IEEE Access