Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree in Tree decision graph (TnT), a framework that extends the conventional decision tree to a more generic and powerful directed acyclic graph. TnT constructs decision graphs by recursively growing decision trees inside the internal or leaf nodes instead of greedy training. The time complexity of TnT is linear to the number of nodes in the graph, and it can construct decision graphs on large datasets. Compared to decision trees, we show that TnT achieves better classification performance with reduced model size, both as a stand-alone classifier and as a base estimator in bagging/AdaBoost ensembles. Our proposed model is a novel, more efficient, and accurate alternative to the widely-used decision trees.

翻译：在许多机器学习应用中,决策树因其轻量和可解释的决策程序而被广泛用作分类师。本文在树决策图(TnT)中引入了树树图(TnT),这是一个框架,将传统决策树扩展至更通用、更强大的定向环绕图。TnT通过内部或叶节内递增决策树而不是贪婪的培训来构建决策图。TnT的时间复杂性是直线到图中的节点数量,它可以在大数据集上构建决策图。与决策树相比,我们显示TnT在模型尺寸缩小的情况下,不仅作为独立分类师,而且作为包建/AdaBoost 集合的基本估计器,能够实现更好的分类性能。我们提议的模型是一种新颖、更高效、更准确的替代广泛使用的决策树的模式。