Variable Importance for Individual classes in R using Caret

Question

I have used a random forest for predicting classes. Now, I am trying to plot variable importance for each class. I have used the below code, but it does not provide me varImp class wise, it is giving me for whole model. Can someone please help me.

Thank you.

odFit = train(x = df_5[,-22], 
              y = df_5$`kpres$cluster`,
              ntree=20,method="rf",metric = "Accuracy",trControl = control,tuneGrid = tunegrid
              )
odFit

varImp(odFit)

Answer 1

Just add importance=TRUE in the train function, which is the same to do importance(odFit) in the randomForest package.

Here a reproducible example:

library(caret)
data(iris)

control <- trainControl(method = "cv",10)
tunegrid <- expand.grid(mtry=2:ncol(iris)-1)
odFit = train(x = iris[,-5], 
              y = iris$Species,
              ntree=20,
              trControl = control,
              tuneGrid = tunegrid,
              importance=T
)
odFit

varImp(odFit)

and here is the output

rf variable importance

  variables are sorted by maximum importance across the classes
             setosa versicolor virginica
Petal.Width   57.21     73.747    100.00
Petal.Length  61.90     79.981     77.49
Sepal.Length  20.01      2.867     40.47
Sepal.Width   20.01      0.000     15.73

you can plot the variable importance with ggplot

library(ggplot2)
vi <- varImp(odFit,scale=T)[[1]]
vi$var <-row.names(vi) 
vi <- reshape2::melt(vi)

ggplot(vi,aes(value,var,col=variable))+
  geom_point()+
  facet_wrap(~variable)