Extending Explainable Ensemble Trees (E2Tree) to regression contexts
This work addresses the lack of transparency in ensemble methods like random forests for regression, offering a tool to help users understand predictions, though it is incremental as it builds on an existing classification method.
The authors extended the Explainable Ensemble Trees (E2Tree) method, originally for classification, to regression tasks, demonstrating its use on real-world datasets to provide graphical explanations of random forest predictions.
Ensemble methods such as random forests have transformed the landscape of supervised learning, offering highly accurate prediction through the aggregation of multiple weak learners. However, despite their effectiveness, these methods often lack transparency, impeding users' comprehension of how RF models arrive at their predictions. Explainable ensemble trees (E2Tree) is a novel methodology for explaining random forests, that provides a graphical representation of the relationship between response variables and predictors. A striking characteristic of E2Tree is that it not only accounts for the effects of predictor variables on the response but also accounts for associations between the predictor variables through the computation and use of dissimilarity measures. The E2Tree methodology was initially proposed for use in classification tasks. In this paper, we extend the methodology to encompass regression contexts. To demonstrate the explanatory power of the proposed algorithm, we illustrate its use on real-world datasets.