The term “voting” can be taken literally here: Each subject with given values of the predictor variables is “dropped through” every tree in the ensemble, so that each single tree returns a predicted class for the subject. The class that most trees “vote” for is returned as the prediction of the ensemble. This democratic voting process is the reason why ensemble methods are also called “committee” methods. Note, however, that there is no diagnostic for the unanimity of the vote. For regression and for predicting probabilities, i.e. relative class frequencies, the results of the single trees are averaged; some algorithms also employ weighted averages. A summary over several aggregation schemes is given in Gatnar (2008). However, even with the simple aggregation schemes used in the standard algorithms, ensembles methods reliably outperform single trees and many other advanced methods (examples of benchmark studies are given in the discussion).
