Random forest or random decision forest is an ensemble learning method for classification, regression, and other tasks that consisting of a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random forest uses bagging and feature randomness to create some uncorrelated trees to prevent decision tree’s overfitting to their training set.