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.
Este ejercicio forma parte del curso
Modeling with tidymodels in R
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
telecom_recipe <- recipe(___, data = ___) %>%
# Removed correlated predictors
___(___) %>%
# Log transform numeric predictors
___(___, base = 10) %>%
# Normalize numeric predictors
___(___) %>%
# Create dummy variables
___(___, ___)