Random forests have only recently been included in standard textbooks on statistical learning, such as Hastie et al. (2009) (while the previous edition, Hastie et al. 2001, did not cover this topic yet). In addition to a short introduction of random forests, this reference gives a thorough background on classification trees and related concepts of resampling and model validation, and is therefore highly recommended for further reading. For the social sciences audience a first instructive review on ensemble methods, including random forests and the related method bagging, was given by Berk (2006). We suggest this reference for the treatment of unbalanced data (for example in the case of a rare disease or mental condition), that can be treated either by means of asymmetric misclassification costs or equivalently by means of weighting with different prior probabilities in classification trees and related methods (see also Chen, Liaw, and Breiman 2004, for the alternative strategy of “down sampling”, i.e., sampling from the majority class as few observations as there are of the minority class), even though the interpretation of interaction effects in Berk
