![]() Īn extension of the algorithm was developed by Leo Breiman and Adele Cutler, who registered "Random Forests" as a trademark in 2006 (as of 2019, owned by Minitab, Inc.). The first algorithm for random decision forests was created in 1995 by Tin Kam Ho using the random subspace method, which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg. ![]() However, data characteristics can affect their performance. : 587–588 Random forests generally outperform decision trees, but their accuracy is lower than gradient boosted trees. Random decision forests correct for decision trees' habit of overfitting to their training set. For regression tasks, the mean or average prediction of the individual trees is returned. For classification tasks, the output of the random forest is the class selected by most trees. Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time.
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