Complete feature engineering pipeline

The recipes package is designed to encode multiple feature engineering steps into one object, making it easier to maintain data transformations in a machine learning workflow.

In this exercise, you will train a feature engineering pipeline to prepare the telecommunications data for modeling.

The telecom_df tibble, as well as your telecom_training and telecom_test datasets from the previous exercises, have been loaded into your workspace.

This exercise is part of the course

Modeling with tidymodels in R

View Course

Exercise instructions

Create a recipe that predicts canceled_service using all predictor variables in the training data.
Remove correlated predictor variables using a 0.8 threshold value.
Normalize all numeric predictors.
Create dummy variables for all nominal predictors.
Train your recipe on the training data and apply it to the test data.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create a recipe that predicts canceled_service using the training data
telecom_recipe <- ___ %>% 
  # Remove correlated predictors
  ___ %>% 
  # Normalize numeric predictors
  ___ %>% 
  # Create dummy variables
  ___

# Train your recipe and apply it to the test data
telecom_recipe %>% 
  ___ %>% 
  ___

Edit and Run Code