Notes on the Robustness of Regression Trees Against Skewed and Contaminated Errors

Regression trees represent one of the most popular tools in predictive data mining applications. However, previous studies have shown that their performances are not completely satisfactory when the dependent variable is highly skewed, and severely degrade in the presence of heavy-tailed error distributions, especially for grossly mis-measured values of the dependent variable. In this paper the lack of robustness of some classical regression trees is investigated by addressing the issue of highly-skewed and contaminated error distributions. In particular, the performances of some non robust regression trees are evaluated through a Monte Carlo experiment and compared to those of some trees, based on M-estimators, recently proposed in order to robustify this kind of methods. In conclusion, the results obtained from the analysis of a real dataset are presented.