Creare un workflow

Con i tuoi dati pronti per l’analisi, dichiarerai un logistic_model() per prevedere se arriveranno in ritardo oppure no.

Assegna il ruolo di "ID" alla variabile flight per mantenerla come riferimento per l’analisi e il debug. A partire dalla variabile date, creerai nuove feature per modellare esplicitamente l’effetto delle festività e rappresentare i factors come variabili dummy.

Raggruppare insieme il tuo model e la recipe() usando workflow() ti aiuterà a garantire che i successivi fitting o le predizioni applichino in modo coerente gli stessi passaggi di feature engineering.

Questo esercizio fa parte del corso

Feature Engineering in R

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Istruzioni dell'esercizio

Assegna un ruolo "ID" a flight.
Metti insieme il modello e la recipe in un oggetto workflow.
Adatta lr_workflow ai dati di test.
Applica tidy() al workflow stimato.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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(___)

Modifica ed esegui il codice

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!