e2tree is built by researchers at the University of Naples Federico II and K-Synth. The goal: make ensemble models auditable without sacrificing accuracy.
Two papers describe the method. The first covers classification, the second extends it to regression.
Aria, M., Gnasso, A., Iorio, C., & Pandolfo, G.
Introduces the algorithm for classification. Defines the co-occurrence dissimilarity framework and the recursive tree-growing procedure, benchmarked on real datasets. The single e2tree matches Random Forest accuracy and is readable.
Aria, M., Gnasso, A., Iorio, C., & Fokkema, M.
Extends the method to regression. Co-occurrence weighting is adapted to account for response similarity between pairs, with a new impurity measure for continuous targets. Benchmarked against CART and GUIDE.
Cite the relevant paper and the R package: