Random Forest nuances

The GC_Random_Forest.pdf file consists of the 500 trees which serve as the homogeneous learners in the random forest ensemble. It is well known that a decision tree has a nice and clear interpretation. This is because it shows how one traverses the path to a terminal node. The random selection of features at each split and the bootstrap samples lead to the setting up of the random forest. Refer to the figure Trees of the Random Forest, which depicts trees numbered 78, 176, 395, and 471. The first split across the four trees is respectively purpose, amount, property, and duration. The second split for the first left side of these four trees is employed, resident, purpose, and amount, respectively. It is a cumbersome exercise to see which variables are meaningful over the others. We know that the earlier a variable appears, the higher its importance is. The question that then arises is, with respect to a random forest, how do we find the depth distribution of the variables...