Tree in Tree: from Decision Trees to Decision Graphs

Abstract

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.

References in corpus (8)

• Scikit-learn: Machine Learning in Python
• A Unified Approach to Interpreting Model Predictions
• Generalized and Scalable Optimal Sparse Decision Trees
• Fast classification using sparse decision DAGs
• Adaptive Neural Trees
• A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
• An Experimental Comparison of Old and New Decision Tree Algorithms
• The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning