In this section, we will go over the methodology of building a basic random forest tree. There are other methods that can be employed, but they all strive to achieve the same goal: diverse trees that serve as the ensemble's base learners.
Understanding random forest trees
Building trees
As mentioned in Chapter 1, A Machine Learning Refresher, create a tree by selecting at each node a single feature and split point, such that the train set is best split. When an ensemble is created, we wish the base learners to be as uncorrelated (diverse) as possible.
Bagging is able to produce reasonably uncorrelated trees by diversifying each tree's train set through bootstrapping. But bagging only diversifies the trees by acting...
