Removing correlated predictors with recipes
Removing correlated predictor variables from your training and test datasets is an important feature engineering step to ensure your model fitting runs as smoothly as possible.
Now that you have discovered that monthly_charges and avg_data_gb are highly correlated, you must add a correlation filter with step_corr() to your feature engineering pipeline for the telecommunications data.
In this exercise, you will create a recipe object that removes correlated predictors from the telecommunications data.
The telecom_training and telecom_test datasets have been loaded into your session.
This exercise is part of the course
Modeling with tidymodels in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Specify a recipe object
telecom_cor_rec <- recipe(___,
data = ___) %>%
# Remove correlated variables
___(___, threshold = ___)