Reduce data using feature importances

Now that you have created a full random forest model, you will explore feature importance.

Even though random forest models naturally — but implicitly — perform feature selection, it is often advantageous to build a reduced model. A reduced model trains faster, computes predictions faster, and is easier to understand and manage. Of course, it is always a trade-off between model simplicity and model performance.

In this exercise, you will reduce the data set. In the next exercise, you will fit a reduced model and compare its performance to the full model. rf_fit, train, and test are provided for you.

The tidyverse, tidymodels, and vip packages have been loaded for you.

This exercise is part of the course

Dimensionality Reduction in R

View Course

Exercise instructions

Use vi() with the rank parameter to extract the ten most important features.
Add the target variable back to the top feature list.
Apply the top feature mask to reduce the data sets.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Extract the top ten features
top_features <- ___ %>% 
  ___(___ = ___) %>% 
  filter(___) %>% 
  pull(Variable)

# Add the target variable to the feature list
top_features <- c(___, "___")

# Reduce and print the data sets
train_reduced <- train[___]
test_reduced <- ___[___]
train_reduced %>% head(5)
test_reduced %>% head(5)

Edit and Run Code