The final ensemble model that we will discuss in this chapter is unique to tree-based models and is known as the random forest. In a nutshell, the idea behind random forests stems from an observation on bagging trees. Let's suppose that the actual relationship between the features and the target variable can be adequately described with a tree structure. It is quite likely that during bagging with moderately sized bootstrapped samples, we will keep picking the same features to split on high up in the tree.
For example, in our Skillcraft data set, we expect to see APM as the feature that will be chosen at the top of most of the bagged trees. This is a form of tree correlation that essentially impedes our ability to derive the variance reduction benefits from bagging. Put differently, the different tree models that we build are not truly independent of each other because they will have many features and split points in common. Consequently, the averaging process at the end will...
