This entry defines and discusses the random forest machine learning algorithm. The algorithm is used to predict class or quantities for target variables using values of a set of predictor variables. It uses decision trees that are generated from bootstrap sampling of the training data set to create a "forest". The entry discusses the algorithm steps, the interpretative tools of the resulting model, current areas of research, and its limitations. Applications to the quantitative geosciences are reviewed as well as availability of software to implement the algorithm.
|Emil D. Attanasi, Timothy Coburn
|USGS Publications Warehouse
|Geology, Energy & Minerals Science Center