  random forests are an ensemble learning method for classification (and regression) that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes output by individual trees. the algorithm for inducing a random forest was developed by leo breiman and adele cutler, and "random forests" is their trademark. the term came from random decision forests that was first proposed by tin kam ho of bell labs in 1995. the method combines breiman's "bagging" idea and the random selection of features, introduced independently by ho and amit and geman in order to construct a collection of decision trees with controlled variance. the selection of a random subset of features is an example of the random subspace method, which, in ho's formulation, is a way to implement classification proposed by eugene kleinberg. == history == the early development of random forests was influenced by the work of amit and geman who introduced the idea of searching over a random subset of the available decisions when splitting a node, in the context of growing a single tree. the idea of random subspace select
Classification_algorithms Decision_trees Ensemble_learning