Extending Explainable Ensemble Trees to Regression Contexts

ABSTRACT The advent of ensemble methods, such as Random Forest (RF), has led to a paradigm shift in supervised learning. These methods have achieved remarkable levels of prediction accuracy by aggregating multiple weak learners. However, a drawback of these methods is their lack of transparency, which often prevents users from understanding their prediction processes. In light of these challenges, Explainable Ensemble Trees (E2Tree) has recently been proposed, providing a graphical representation of the relationships between response variables and predictors in RFs for classification. E2Tree constructs a single decision tree based on (dis)similarities between observations. By summarizing both distances in terms of predictors and a forest as a single decision tree, E2Tree merges the strengths of both decision trees and decision tree ensembles. In this paper, we propose to extend the E2Tree methodology to regression contexts. We investigate the performance of E2Tree for regression using real‐world datasets. We use the Mantel test to test the correlation between similarities of the RF and E2Tree.