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
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