Interesting applications of random forests in data sets of lower dimensionality include the studies of Rossi, Amaddeo, Sandri, and Tansella (2005) on determinants of once-only contact in community mental health service and Baca-Garcia, Perez-Rodriguez, Saiz-Gonzalez, Basurte-Villamor, Saiz-Ruiz, Leiva-Murillo, de Prado-Cumplido, Santiago-Mozos, Artes-Rodriguez, and de Leon (2007) on attempted suicide under consideration of the family history. For detecting relevant predictor variables, Rossi et al. (2005) point out that the random forest variable importance ranking proves to be more stable than stepwise variable selection approaches available for logistic regression, that are known to be affected by order effects (see, e.g., Freedman 1983; Derksen and Keselman 1992; Austin and Tu 2004). Moreover, a high random forest variable importance of a variable that was not included in stepwise regression may indicate that the variable works in interactions that are too complex to be captured by parametric regression models. As another advantage, Marinic, Supek, Kovacic, Rukavina, Jendricko, and Kozaric-Kovacic (2007) point out in an application to the diagnosis of posttraumatic stress disorder, that random forests can be used to automatically generate realistic estimates of the prediction accuracy on test data by means of repeated random sampling from the learning data.
