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. They operate by constructing a multitude of decision trees at training time and outputting the mode of the classes in the individual trees (for classification) or the mean value across all trees (for regression).
(Adapted from 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 (bagging, or bootstrap aggregating), and (b) using a random subsample of the explanatory variables at each node of each tree.
References
- Random Forest page maintained by Leo Breiman and Adele Cutler, the creators of the algorithm.
- Wikipedia pages on Random Forest and Ensemble Learning.
