%0 Journal Article %1 Marton2023-op %A Marton, Sascha %A Lüdtke, Stefan %A Bartelt, Christian %A Stuckenschmidt, Heiner %D 2023 %K GradTree Learning Zno axis-aligned decision descent gradient trees %T GradTree: Learning axis-aligned decision trees with gradient descent %X 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

@article{Marton2023-op, 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}, added-at = {2025-01-07T15:21:09.000+0100}, author = {Marton, Sascha and L{\"u}dtke, Stefan and Bartelt, Christian and Stuckenschmidt, Heiner}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/28db484d92220c0eb5f2860aaccda8247/scadsfct}, eprint = {2305.03515}, interhash = {70715c13901f23d49c08857692cf8c94}, intrahash = {8db484d92220c0eb5f2860aaccda8247}, keywords = {GradTree Learning Zno axis-aligned decision descent gradient trees}, primaryclass = {cs.LG}, timestamp = {2025-01-31T11:42:58.000+0100}, title = {{GradTree}: Learning axis-aligned decision trees with gradient descent}, year = 2023 }