Another recent ensemble method is called 'random forests'. In this technique, a bootstrap sample of the training data is chosen. At the root node, a small random sample of explanatory variables is selected and the best split made using that limited set of variables. At each subsequent node, another small random sample of the explanatory variables is chosen, and the best split made. The tree continues to be grown in this fashion until it reaches the largest possible size, and is left unpruned. The whole process, starting with a new bootstrap sample, is repeated a large number of times. As in committee models, the final prediction is a (weighted) plurality vote or average from prediction of all the trees in the collection.
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