For those new to Random Forests, it is a powerful ensemble technique developed by Leo Breiman and Adele Cutler at the University of California, Berkeley, and is favored by many predictive modeling practitioners. The deceptive simplicity of the algorithm builds hundreds of independent trees and employs lots of sampling from both observations and variables. Random Forests’ unique ability to evaluate unbiased model performance based on the out-of-bag data removes the need to have a separate testing/validation sample. This immediately positions Random Forests as the top predictive modeling tool in the wide data applications where the number of variables exceeds, often many times over, the number of available observations.