GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
Sascha Marton,
Stefan Lüdtke,
Christian Bartelt
et al.
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 gra… Show more
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