Feature engineering process
To incorporate feature engineering into the modeling process, the training and test datasets must be preprocessed before the model fitting stage. With the new skills you have learned in this chapter, you will be able to use all of the available predictor variables in the telecommunications data to train your logistic regression model.
In this exercise, you will create a feature engineering pipeline on the telecommunications data and use it to transform the training and test datasets.
The telecom_training and telecom_test datasets as well as your logistic regression model specification, logistic_model, 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.
telecom_recipe <- recipe(___, data = ___) %>%
# Removed correlated predictors
___(___) %>%
# Log transform numeric predictors
___(___, base = 10) %>%
# Normalize numeric predictors
___(___) %>%
# Create dummy variables
___(___, ___)