让浅层决策树一次判断多个阈值，提升准确率同时保持路径简洁。
Adaptive Multi-Branching for Shallow Decision Tree Induction
- 通过可微多路分裂，单个节点同时处理多个特征阈值。
- 在21个数据集上平均准确率领先，深度受限下表现最优。
- 适合需要短路径高精度的场景，如医疗诊断或金融风控。
决策树因其可解释性在表格预测任务中备受青睐，但受限于最大深度时，传统二叉树表达能力不足，因每个内部节点仅做一次阈值判断。本文研究浅层树构造，目标是在保持根到叶路径短的前提下提升准确率。提出多分支神经决策树（MBNDT），一种端到端训练的轴对齐单棵树，支持可微多路分裂。每个内部节点学习选定特征上的有序阈值及自适应分支掩码，动态调整有效分支数，训练后转换为确定性单路径树用于推理。在21个OpenML二分类基准上，MBNDT在深度约束的单树基线中取得最佳平均排名和平均平衡准确率；受控消融实验确认多路分裂是性能提升主因。该方法带来明确权衡：相比其他单树基线，其生成更多叶子节点，因此最适合在短路径、有界决策路径下优先追求精度的场景。
原文摘要 · Abstract (English)
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.
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