  The random forest algorithm, proposed by L. Breiman in 2001, has been
extremely successful as a general-purpose classification and regression method.
The approach, which combines several randomized decision trees and aggregates
their predictions by averaging, has shown excellent performance in settings
where the number of variables is much larger than the number of observations.
Moreover, it is versatile enough to be applied to large-scale problems, is
easily adapted to various ad-hoc learning tasks, and returns measures of
variable importance. The present article reviews the most recent theoretical
and methodological developments for random forests. Emphasis is placed on the
mathematical forces driving the algorithm, with special attention given to the
selection of parameters, the resampling mechanism, and variable importance
measures. This review is intended to provide non-experts easy access to the
main ideas.
