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.

Deze oefening maakt deel uit van de cursus

Modeling with tidymodels in R

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Oefeninstructies

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.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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 %>% 
  ___ %>% 
  ___

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