Rather than striving for the most efficient split at each round of recursive partitioning, an alternative technique is to construct multiple trees and combine their predictions to select an optimal path of classification or prediction. This involves a randomized selection of binary questions to grow multiple different decision trees, known as random forests. In the industry, you will also often hear people refer to this process as “bootstrap aggregating” or “bagging.”
