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.

Cet exercice fait partie du cours

Feature Engineering in R

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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.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de 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(___)

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Feature Engineering in R

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You’ll wrap up the course by learning about feature engineering and machine learning techniques. You’ll begin by focusing on the problems associated with using all available features in a model and the importance of identifying irrelevant and redundant features and learning to remove these features using embedded methods such as lasso and elastic-net. Next, you’ll explore shrinkage methods such as lasso, ridge, and elastic-net, which can be used to regularize feature weights or select features by setting coefficients to zero. Finally, you’ll finish by focusing on creating an end-to-end feature engineering workflow and reviewing and practicing the previously learned concepts and functions in a small project.

Exercise 1: Reducing the model's features Exercise 2: Sifting through variable importance Exercise 3: Assessing model performance using all available predictors Exercise 4: Building a reduced model Exercise 5: Shrinkage methods Exercise 6: Manual regularization with Lasso Exercise 7: Tuning the penalty Exercise 8: Finalizing the model Exercise 9: Putting it all together Exercise 10: Prep and split Exercise 11: Preprocess Exercise 12: Model Exercise 13: Assess Exercise 14: Congratulations!