Figure 2

Random forest model. Example of training and classification processes using random forest. A) Each decision tree in the ensemble is built upon a random bootstrap sample of the original data, which contains positive (green labels) and negative (red labels) examples. B) Class prediction for new instances using a random forest model is based on a majority voting procedure among all individual trees. The procedure carried out for each tree is as follows: for each new data point (i.e., X), the algorithm starts at the root node of a decision tree and traverse down the tree (highlighted branches) testing the variables values in each of the visited split nodes (pale pink nodes), according to each it selects the next branch to follow. This process is repeated until a leaf node is reached, which assigns a class to this instance: green nodes predict for the positive class, red nodes predict for the negative class. At the end of the process, each tree casts a vote for the preferred class label, and the mode of the outputs is chosen as the final prediction.