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.

Create a recipe object, telecom_recipe, that sets the outcome variable to canceled_service and all remaining columns in telecom_training to predictor variables.
Using selector functions, remove correlated predictors at a 0.8 threshold, log transform all numeric predictors, normalize all numeric predictors, and create dummy variables for all nominal predictor variables.

Exercise

Feature engineering process

Instructions 1/3