Multiple feature engineering steps
The power of the recipes package is that you can include multiple preprocessing steps in a single recipe object. These steps will be carried out in the order they are entered with the step_*() functions.
In this exercise, you will build upon your feature engineering from the last exercise. In addition to removing correlated predictors, you will create a recipe object that also normalizes all numeric predictors in 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_norm_rec <- ___ %>%
# Remove correlated variables
___ %>%
# Normalize numeric predictors
___