Get startedGet started for free

Building a workflow

With your data ready for analysis, you will declare a logistic_model() to predict whether or not they will arrive late.

You assign the role of "ID" to the flight variable to keep it as a reference for analysis and debugging. From the date variable, you will create new features to explicitly model the effect of holidays and represent factors as dummy variables.

Bundling your model and recipe() together using workflow()will help ensure that subsequent fittings or predictions will implement consistent feature engineering steps.

This exercise is part of the course

Feature Engineering in R

View Course

Exercise instructions

  • Assign an "ID" role to flight.
  • Bundle the model and the recipe into a workflow object.
  • Fit lr_workflow to the test data.
  • Tidy the fitted workflow.

Hands-on interactive exercise

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

lr_model <- logistic_reg()

# Assign an "ID" role to flight
lr_recipe <- recipe(arrival ~., data = train) %>% update_role(flight, new_role = ___) %>%
  step_holiday(date, holidays = timeDate::listHolidays("US")) %>% step_dummy(all_nominal_predictors())

# Bundle the model and the recipe into a workflow object
lr_workflow <- workflow() %>% add_model(___) %>% add_recipe(___)
lr_workflow

# Fit lr_workflow workflow to the test data  
lr_fit <- lr_workflow %>% ___(data = test)

# Tidy the fitted workflow  
tidy(___)
Edit and Run Code