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This research introduces the Multi-Branch Neural Decision Tree (MBNDT), which enhances shallow-depth models by enabling differentiable multi-way splits to overcome the expressiveness limits of traditional binary trees. The approach achieves superior balanced accuracy on standard benchmarks while maintaining short decision paths, though it does so at the cost of a larger leaf count compared to baseline single-tree methods. This work offers a concrete architectural advancement for scenarios where interpretability and path length constraints are prioritized alongside predictive performance.
Adaptive Multi-Branching for Shallow Decision Tree Induction
arXiv:2608.29262v1 Announce Type: cross Abstract: Decision trees are attractive for tabular prediction tasks because each prediction follows an interpretable sequence of feature-threshold tests. Under a strict maximum-depth budget, however, conventional binary trees can be under-expressive, since each internal node makes only a single threshold decision. We study shallow-depth tree induction, where the goal is to improve accuracy while keeping root-to-leaf paths short. We propose the Multi-Branch Neural Decision Tree with Adaptive Pruning (MBNDT), a single axis-aligned tree trained end-to-end with differentiable multi-way splits. Each internal node learns ordered thresholds over a selected feature and a branch mask that adapts its effective arity, and the trained model is converted to a deterministic single-path tree for inference. Across 21 OpenML binary-classification benchmarks, MBNDT achieves the best average rank and mean balanced accuracy among depth-constrained single-tree baselines; a controlled ablation isolates multi-way splitting as the source of the gain. These gains come with an explicit trade-off: MBNDT realizes more leaves than the other single-tree baselines, making it best suited when accuracy under short, bounded decision paths is prioritized over minimal global tree size.
Abstract:Decision trees are attractive for tabular prediction tasks because each prediction follows an interpretable sequence of feature-threshold tests. Under a strict maximum-depth budget, however, conventional binary trees can be under-expressive, since each internal node makes only a single threshold decision. We study shallow-depth tree induction, where the goal is to improve accuracy while keeping root-to-leaf paths short. We propose the Multi-Branch Neural Decision Tree with Adaptive Pruning (MBNDT), a single axis-aligned tree trained end-to-end with differentiable multi-way splits. Each internal node learns ordered thresholds over a selected feature and a branch mask that adapts its effective arity, and the trained model is converted to a deterministic single-path tree for inference. Across 21 OpenML binary-classification benchmarks, MBNDT achieves the best average rank and mean balanced accuracy among depth-constrained single-tree baselines; a controlled ablation isolates multi-way splitting as the source of the gain. These gains come with an explicit trade-off: MBNDT realizes more leaves than the other single-tree baselines, making it best suited when accuracy under short, bounded decision paths is prioritized over minimal global tree size.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2608.29262 [cs.LG]
(or arXiv:2608.29262v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.29262 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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