In the previous chapter, we learned about how random forests improve the predictions made by individual decision trees by combining them into an ensemble that reduces the high variance of individual trees. Random forests use bagging, which is short for bootstrap aggregation, to introduce random elements into the process of growing individual trees.
More specifically, bagging draws samples from the data with replacement so that each tree is trained on a different but equal-sized random subset of the data (with some observations repeating). Random forests also randomly select a subset of the features so that both the rows and the columns of the data that are used to train each tree are random versions of the original data. The ensemble then generates predictions by averaging over the outputs of the individual trees.
Individual trees are usually grown deep...
