A Random Forest Guided Tour
It provides a comprehensive guide for researchers and practitioners to understand and apply random forests, but it is incremental as it synthesizes existing knowledge rather than introducing new methods.
This paper reviews recent theoretical and methodological developments in the random forest algorithm, focusing on its mathematical foundations, parameter selection, resampling mechanisms, and variable importance measures to make the concepts accessible to non-experts.
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.