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
- Random Forest page maintained by Leo Breiman and Adele Cutler, the creators of the algorithm.
- Wikipedia pages on Random Trees, Random Forest and Ensemble Learning.
