Exploring recipe objects

The first step in feature engineering is to specify a recipe object with the recipe() function and add data preprocessing steps with one or more step_*() functions. Storing all of this information in a single recipe object makes it easier to manage complex feature engineering pipelines and transform new data sources.

Use the R console to explore a recipe object named telecom_rec, which was specified using the telecom_training data from the previous chapter and the code below.

telecom_rec <- recipe(canceled_service ~ .,
                      data = telecom_df) %>% 
  step_log(avg_call_mins, base = 10)

Both telecom_training and telecom_rec have been loaded into your session.

How many numeric and nominal predictor variables are encoded in the telecom_rec object?

Possible answers

There are 2 numeric and 4 nominal predictor variables.

There are 4 numeric and 5 nominal predictor variables.

There are 5 numeric and 3 nominal predictor variables.

There are 3 numeric and 4 nominal predictor variables.

Exercise

Exploring recipe objects

Instructions

Possible answers