One method of improving the accuracy of CART models is to build multiple decision trees, not just the one. In random forests, we do just that—a large number of CART trees are generated and thereafter, each tree in the forest votes on the outcome, with the majority outcome taken as the final prediction.
To generate a random forest, a process known as bootstrapping is employed whereby the training data for each tree making up the forest is selected randomly with replacement. Therefore, each individual tree will be trained using a different subset of independent variables and, hence, different training data.
