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Random forest is a machine-learning method based on combining the outputs of many decision trees.

Overview

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.

(https://en.wikipedia.org/wiki/Random_forest).

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.

References

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