A Random Forest Guided Tour

TL;DR

This paper reviews recent theoretical and methodological advances in random forests, highlighting their success, versatility, and the mathematical principles behind their operation, including parameter selection and variable importance.

Contribution

It provides a comprehensive overview of the latest developments in random forest theory and methodology, making complex ideas accessible to non-experts.

Findings

Random forests perform well with many variables and few observations.

Recent theoretical insights explain the algorithm's effectiveness.

Methodological improvements enhance parameter tuning and variable importance measures.

Abstract

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.