EDiT: Interpreting ensemble models via compact soft decision trees

Abstract

Given feature-based data, how can we accurately classify individual input and interpret the result of it? Ensemble models are often the best choice in terms of accuracy when dealing with feature-based datasets. However, interpreting the decision made by the ensemble model for individual input seems intractable. On the other hand, decision trees, although being prone to overfit, are considered as the most interpretable in terms of being able to trace the decision process of individual input. In this work, we propose Ensemble to Distilled Tree (EDiT), a novel distilling method that generates compact soft decision trees from ensemble models. EDiT exploits the interpretability of a tree-based structure by removing redundant branches and learning sparse weights, while enhancing accuracy by distilling the knowledge of ensemble models such as random forests (RF). Our experiments on eight datasets show that EDiT reduces the number of parameters of an RF by 6.4 to 498.4 times with a minor loss of classification accuracy.