GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
Abstract
Decision Trees (DTs) are commonly used for many machine learning tasks due to their high degree of interpretability. However, learning a DT from data is a difficult optimization problem, as it is non-convex and non-differentiable. Therefore, common approaches learn DTs using a greedy growth algorithm that minimizes the impurity locally at each internal node. Unfortunately, this greedy procedure can lead to inaccurate trees. In this paper, we present a novel approach for learning hard, axis-aligned DTs with gradient descent. The proposed method uses backpropagation with a straight-through operator on a dense DT representation, to jointly optimize all tree parameters. Our approach outperforms existing methods on binary classification benchmarks and achieves competitive results for multi-class tasks. The method is available under: https://github.com/s-marton/GradTree
Cite
@article{arxiv.2305.03515,
title = {GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent},
author = {Sascha Marton and Stefan Lüdtke and Christian Bartelt and Heiner Stuckenschmidt},
journal= {arXiv preprint arXiv:2305.03515},
year = {2024}
}