Hi, For a binary classification model, why variable importance produces separate importance for each outcome? What does column N and R stand for? I thought only the first column 'all' is needed. (i.e. randomly permute variable x, calculated change in out-of-bag prediction error. Code *model <- rfsrc(status ~ ., data = breast)* *x <- predict(model)* *x$importance * produces the following * all N R mean_radius 2.880195e-03 2.175676e-03 -2.543478e-03 mean_texture 1.129752e-03 7.837838e-04 -8.043478e-04 mean_perimeter 1.976838e-03 1.466216e-03 -1.652174e-03 mean_area 3.143459e-03 2.054054e-03 -1.782609e-03* ...... Thanks a lot! Turing [[alternative HTML version deleted]]