How can we effectively find the best structures in tree models? Tree models have been favored over complex black box models in domains where interpretability is crucial for making irreversible decisions. However, searching for a tree structure that gives the best balance between the performance and the interpretability remains a challenging task. In this paper, we propose TART (Transition Matrix Representation with Transposed Convolutions), our novel generalized tree representation for optimal structural search. TART represents a tree model with a series of transposed convolutions that boost the speed of inference by avoiding the creation of transition matrices. As a result, TART allows one to search for the best tree structure with a few design parameters, achieving higher classification accuracy than those of baseline models in feature-based datasets.

翻译：如何在树模型中有效地找到最佳结构? 树模型在解释性决定不可逆转的关键领域,优于复杂的黑盒模型。 然而,寻找一种在性能和可解释性之间取得最佳平衡的树结构仍然是一项艰巨的任务。 在本文中,我们提议使用我们的新颖的通用树图示来进行最佳的结构搜索。 TART代表着一种树模型,它包含一系列转换变异,通过避免创建过渡矩阵来加快推论速度。 结果, TART允许人们寻找具有少数设计参数的最佳树结构,实现比基于地物的数据集基准模型更高的分类准确性。