Random Forests Accelerator

8th August 2014

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, currently used by Salford Systems in the US. 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 a random selection of features in order to construct a collection of decision trees with controlled variance.

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