library(randomForest) data(iris) fit <- randomForest(Species ~ ., data=iris, importance=TRUE); fit.imp<-importance(fit) fit.imp
columns 1-3 show the class-specific variable importance for the Mean Decrease Acuracy measure. Note that for Sepal.Length, the class-specific VIs are lower than the Mean VI values for Accuracy.
I have two questions about the implementation of RandomForest in R:
1) How are the class-specific importances calculated (i.e., how is it possible for the class means to be lower than the total mean?)? I understand the theory of how permutation accuracy is calculated, but I am not a mathematician so reading the raw equations doesn't help me much. Any quick explanation would be much appreciated before I dive into the RF package source code.
2) Is there a way to calculate class-specific Gini metrics, not class-specific Accuracy metrics (the default)? I really want to do this. I was about to start trying to code a way to do it, but thought I would ask here first.