The random forest (RF) is an “ensemble learning” technique consisting of the aggregation of a large number of decision trees, resulting in a reduction of variance compared to the single decision trees. In this paper we consider Leo Breiman’s original version of RF [2], while acknowledging that other variants exist, for example RF based on conditional inference trees [13] which address the problem of variable selection bias [14] and perform better in some cases, or extremely randomized trees [15].
