As already mentioned, the main advantage of the random forest permutation accuracy importance, as compared to univariate screening methods, is that it covers the impact of each predictor variable individually as well as in multivariate interactions with other predictor variables. For example Lunetta et al. (2004) find that genetic markers relevant in interactions with other markers or environmental variables can be detected more efficiently by means of random forests than by means of univariate screening methods like Fisher’s exact test.
