Random Forest or Random Decision Forest are an ensemble learning method for classification, regression, and other tasks, that operate by constructing 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 decision forests correct for decision trees habit of overfitting to their training set. The first algorithm for random decision forests was created by Tin Kam Ho using the random subspace method, which, in Ho’s formulation, is a way to implement the “stochastic discrimination” approach to the classification proposed by Eugene Kleinberg. An extension of the algorithm was developed by Leo Breiman and Adele Cutler and “Random Forests” is their trademark. The extension combines Breiman’s “bagging” idea and random selection of features introduced first by Ho and later independently by Amit and Geman in order to construct a collection of decision trees with controlled variance. Decision trees are a popular method for various machine learning tasks. Tree learning comes closest to meeting the requirements for serving as an off-the-shelf procedure for data mining because it is invariant under scaling and various other transformations of feature values, is robust to the inclusion of irrelevant features and produces inspectable models.
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