  In this paper we apply the previously introduced approximation method based
on the ANOVA (analysis of variance) decomposition and Grouped Transformations
to synthetic and real data. The advantage of this method is the
interpretability of the approximation, i.e., the ability to rank the importance
of the attribute interactions or the variable couplings. Moreover, we are able
to generate an attribute ranking to identify unimportant variables and reduce
the dimensionality of the problem. We compare the method to other approaches on
publicly available benchmark datasets.
