I am using random forests to generate regression trees. My dataset has 30k observations of 15 variables.
Each tree I generate is limited to a 4 nodes depth (including terminal leaves) and to a sampling of 6 variables amongst the 16.
Problem here is the 2 variables DPS_g_0 and DPS_g_1 are used several time in the same branch. It means that the algo can't find a better variable to split the subset, than the same it has used on the upper level.
Is using that kind of tree a sign of an overfitted random forest?
Thanks for your help