Increasing Accuracy and Explainability in Fuzzy Regression Trees: An Experimental Analysis

Regression Trees (RTs) have been widely used in the last decades in various domains, also thanks to their inherent explainability. Fuzzy RTs (FRTs) extend RTs by using fuzzy sets and have proven to be particularly suitable for dealing with noisy and/or uncertain environments. The modelling capability of FRTs depends, among other factors, on the model used in the leaves for determining the output, and on the inference strategy. Nevertheless, the impact of such factors on FRTs accuracy and explainability has not been adequately investigated. In this paper, we extend a recently proposed learning scheme for FRTs by employing both linear models in the leaves and the maximum matching inference strategy. The former extension aims to increase accuracy, and the latter to improve explainability. We carried out an extensive experimental analysis by comparing the four FRT versions corresponding to any possible combination of the two extensions introduced in the paper. The results show that the best trade-off between accuracy and explainability is obtained by employing both of them.