terms of bound for generalization error) of a brode class of composite decision trees and forests is given in [@Salvo:16]. Although it is formally incorrect to compare these results to our setting, our bounds have the same order of magnitude in terms of the sample size and number of leaves as in [@Mansur:99]. Furthermore, the error bounds of scale linearly with the number of leaves, whereas we have sublinear scale ($\sqrt{\ell}$) while having the same order of magnitude in the number of training samples ($1/\sqrt{n}$). In the regression setting the recent work [@Lauer:17] author considers the theoretical analysis of the switching regression framework (which includes the framework of regression decision trees). However, this framework does not consider the regularization of the models on the partitioned space. In such setting, using similar toolbox of the Rademacher complexity for statistical error conctrol, in Equation $(16)$ author obtains risk upper bound which scales linearly with the number of partition cells (which corresponds to the number of leaves in regression trees scenario and comparably worse to our setting). Also, author provides only the majorant term in the upper bound, whereas (as we stressed out before) in our paper we investigate the influence of the 