Pick Your Flavor of Random Forest

The ModelMap package (Freeman, 2009) for R (R Development Core Team, 2008) has added two additional variants of random forests: quantile regression forests and conditional inference forests. The quantregForest package (Meinshausen and Schiesser, 2015) is used for quantile regression forest (QRF) models. QRF models provide the ability to map the predicted median and individual quantiles. This makes it possible to map lower and upper bounds for the predictions without relying on the assumption that the predictions of individual trees in the model follow a normal distribution. The party package (Hothorn et al., 2006; Strobl et al., 2007, 2008) is used for conditional inference forest (CF) models. CF models offer two advantages over traditional RF models: they avoid RF’s bias towards predictor variables with higher numbers of categories; and, they provide a conditional importance measure, allowing a better understanding of the relative importance of correlated predictor variables.