Figure 3
From: An AUC-based permutation variable importance measure for random forests
Distribution of AUC-values for 100 simulated datasets for AUC-based (filled) and error-rate-based (unfilled) permutation VIMs for different class imbalances. The AUC is used to assess the ability of a VIM to discriminate between predictors with an effect and predictors without an effect. Distributions are shown for total sample sizes of n = 100 (left panel), n = 500 (middle panel) and n = 1000 (right panel).