extracting more than 20 variables by importance via varImp

Question

I'm dealing with a large dataset that involves more than 100 features (which are all relevant because they have already been filtered; the original dataset had over 500 features). I created a random forest model via the train() function from the caret package and using the "ranger" method.

Here's the question: how does one extract all of the variables by importance, as opposed to only the top 20 most important variables? The varImp() function yields only the top 20 variables by default.

Here's some sample code (minus the training set, which is very large):

library(caret)
rforest_model <- train(target_variable ~ .,
                       data = train_data_set,
                       method = "ranger",
                       importance = "impurity)

And here's the code for extracting variable importance:

varImp(rforest_model)

Answer 1

The varImp function extracts importance for all variables (even if they are not used by the model), it just prints out the top 20 variables. Consider this example:

library(mlbench) #for data set
library(caret)
library(tidyverse)

set.seed(998)
data(Ionosphere)

rforest_model <- train(y = Ionosphere$Class,
                       x = Ionosphere[,1:34],
                       method = "ranger",
                       importance = "impurity")

nrow(varImp(rforest_model)$importance) #34 variables extracted

lets check them:

varImp(rforest_model)$importance %>% 
  as.data.frame() %>%
  rownames_to_column() %>%
  arrange(Overall) %>%
  mutate(rowname = forcats::fct_inorder(rowname )) %>%
  ggplot()+
    geom_col(aes(x = rowname, y = Overall))+
    coord_flip()+
    theme_bw()

note that V2 is a zero variance feature in this data set hence it has 0 importance and is not used by the model at all.