Random Forest
Random forest is ensemble learning methods for classification, regression and other tasks. Random forest classifiers create a set of decision trees from randomly selected subsets of the training set. The votes from different decision trees are then combined to assign the class of the test object. They output a class that is either a mode of the classes defined (classification), or a mean prediction (regression) of the individual treess defined. Random decision forests correct for decision trees overfitting to the training set.
Applications
Pathway analysis and genetics association and epistasis detection ref.
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