Figure 2.

This figure illustrates the training on an RF classifier. RF is an ensemble machine learning algorithm. Let n be the number of trees in the random forest classifier; n different training sets are then generated using the bootstrapping technique, and for each training set, 1 decision tree is generated. The ovals in the trees represent the splits, while the rectangles represent the classes. While generating each tree, the most effective feature out of a random subset of features would be selected to create the splits. Gini's diversity index is a commonly used split criterion. During the phase of testing, features of new samples would be passed along all the trees. Each tree would vote for a decision, and the majority of the votes would represent the final decision. RF indicates random forest.