AbstractRandom forests have emerged as a versatile and highly accurate classification and regression methodology, requiring little tuning and providing interpretable outputs. Here, we briefly outline the genesis of, and motivation for, the random forest paradigm as an outgrowth from earlier tree‐structured techniques. We elaborate on aspects of prediction error and attendant tuning parameter issues. However, our emphasis is on extending the random forest schema to the multiple response setting. We provide a simple illustrative example from ecology that showcases the improved fit and enhanced interpretation afforded by the random forest framework. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 80‐87 DOI: 10.1002/widm.12This article is categorized under: Algorithmic Development > Hierarchies and Trees Algorithmic Development > Ensemble Methods Technologies > Machine Learning Technologies > Prediction