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Decision trees are an extremely popular machine learning technique.Unfortunately, overfitting in decision trees still remains an open issue thatsometimes prevents achieving good performance. In this work, we present a novelapproach for the construction of decision trees that avoids the overfitting bydesign, without losing accuracy. A distinctive feature of our algorithm is thatit requires neither the optimization of any hyperparameters, nor the use ofregularization techniques, thus significantly reducing the decision treetraining time. Moreover, our algorithm produces much smaller and shallowertrees than traditional algorithms, facilitating the interpretability of theresulting models.

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