  We study the effectiveness of subagging, or subsample aggregating, on
regression trees, a popular non-parametric method in machine learning. First,
we give sufficient conditions for pointwise consistency of trees. We formalize
that (i) the bias depends on the diameter of cells, hence trees with few splits
tend to be biased, and (ii) the variance depends on the number of observations
in cells, hence trees with many splits tend to have large variance. While these
statements for bias and variance are known to hold globally in the covariate
space, we show that, under some constraints, they are also true locally.
Second, we compare the performance of subagging to that of trees across
different numbers of splits. We find that (1) for any given number of splits,
subagging improves upon a single tree, and (2) this improvement is larger for
many splits than it is for few splits. However, (3) a single tree grown at
optimal size can outperform subagging if the size of its individual trees is
not optimally chosen. This last result goes against common practice of growing
large randomized trees to eliminate bias and then averaging to reduce variance.
