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

## TL;DR

This paper introduces a new hyperparameter-free decision tree construction method that inherently prevents overfitting, resulting in smaller, more interpretable trees without sacrificing accuracy.

## Contribution

The proposed approach eliminates the need for hyperparameter tuning and regularization, reducing training time and producing simpler, more interpretable decision trees.

## Key findings

- Avoids overfitting by design
- Produces smaller, shallower trees
- Reduces training time significantly

## 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.