  Random forests are ensemble learning methods introduced by Breiman (2001)
that operate by averaging several decision trees built on a randomly selected
subspace of the data set. Despite their widespread use in practice, the
respective roles of the different mechanisms at work in Breiman's forests are
not yet fully understood, neither is the tuning of the corresponding
parameters. In this paper, we study the influence of two parameters , namely
the subsampling rate and the tree depth, on Breiman's forests performance. More
precisely, we show that fully developed sub-sampled forests and pruned (without
subsampling) forests have similar performances, as long as respective
parameters are well chosen. Moreover , experiments show that a proper tuning of
subsampling or pruning lead in most cases to an improvement of Breiman's
original forests errors.
