This technique utilizes “bagging” to sample the training dataset to produce
multiple decision trees. During the classification stage, these trees are then used in concert to produce
several classification results. Each tree casts a vote for classification of the data point, and the consensus
data point (i.e., the one with the plurality of votes) is assigned.