Random Forests

Bagging regression trees is a technique that can turn a single tree model with high variance and poor predictive power into a fairly accurate prediction function. Unfortunately, bagging regression trees typically suffers from tree correlation, which reduces the overall performance of the model. Random forests are a modification of bagging that builds a large collection of de-correlated trees and have become a very popular “out-of-the-box” learning algorithm that enjoys good predictive performance. This latest tutorial will cover the fundamentals of random forests.