GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent

arXiv:2305.03515v5 Announce Type: replace
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 …

arxiv cs.ai cs.lg decision decision trees gradient trees type