Boosting Your Trading Strategy

In the previous chapter, we saw how random forests improve on the predictions of a decision tree by combining many trees into an ensemble. The key to reducing the high variance of an individual tree is the use of bagging, short for bootstrap aggregation, which introduces randomness into the process of growing individual trees. More specifically, bagging samples from the data with replacements so that each tree is trained on a different but equal-sized random subset, with some observations repeating. In addition, a random forest randomly selects a subset of the features so that both the rows and the columns of the training set for each tree are random versions of the original data. The ensemble then generates predictions by averaging over the outputs of the individual trees.

Individual random forest trees are usually grown deep to ensure low bias while relying on the randomized training process to produce different, uncorrelated prediction errors...