Random forest is a machine-learning classifier based on choosing random subsets of variables for each tree and using the most frequent tree output as the overall classification.


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 randomness in the decision trees follows from (a) using a new bootstrapped version of the original sample for each tree and (b) using a random subsample of the explanatory variables at each node of each tree.