An AUC-based permutation variable importance measure for random forests
An AUC-based permutation variable importance measure for random forests
The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, …