Identification of Statistically Significant Features from Random Forests

Embedded feature selection can be performed by analyzing the variables used in a Random Forest. Such a multivariate selection takes into account the interactions between variables but is not easy to interpret in a statistical sense. We propose a statistical procedure to measure variable importance that tests if variables are significantly useful in combination with others in a forest. We show experimentally that this new importance index correctly identifies relevant variables. The top of the variable ranking is, as expected, largely correlated with Breiman’s importance index based on a permutation test. Our measure has the additional benefit to produce p-values from the forest voting process. Such p-values offer a very natural way to decide which features are significantly relevant while controlling the false discovery rate.