I would recommend using MDS since it gives the best summary at a glance.
But in favor of interpretability, after looking at variable importances, you could pick the
k most significant variables (if you have a large number of predictors) and use
classCenter function on the proximity matrix. This will let you visualize the representative point for each class, for every variable.
iris.rf <- randomForest(iris[,-5], iris[,5], prox=TRUE)
iris.p <- melt(classCenter(iris[,-5], iris[,5], iris.rf$prox), id=rownames())
iris.m <- melt(iris, id='Species', variable.name = 'Var', value.name = 'Measurement')
names(iris.p) <- names(iris.m)
ggplot(iris.m, aes(x=Species, color=Species)) +
geom_point(data=iris.p,aes(y=Measurement), size=4, pch=15, color='grey40') +
If you have new points with Species prediction, simply add another
geom_point layer with the new data and change the
pch attributes to differentiate from old points. This will let you visualize the new points relative to training data as well as class representatives from training data.
Alternatively, you could take the variables pairwise and see the class prototypes/ centers for each variable combination. The help file
?classCenter gives an example of that.