Membangun workflow

Dengan data Anda siap untuk dianalisis, Anda akan mendeklarasikan logistic_model() untuk memprediksi apakah mereka akan tiba terlambat atau tidak.

Anda menetapkan peran "ID" pada variabel flight untuk menyimpannya sebagai referensi untuk analisis dan debugging. Dari variabel date, Anda akan membuat fitur baru untuk secara eksplisit memodelkan efek hari libur dan merepresentasikan factors sebagai variabel dummy.

Membundel model dan recipe() bersama menggunakan workflow() akan membantu memastikan bahwa proses pelatihan atau prediksi selanjutnya menerapkan langkah-langkah feature engineering yang konsisten.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Tetapkan peran "ID" pada flight.
Bundel model dan recipe ke dalam objek workflow.
Pasangkan lr_workflow ke data test.
Rapikan workflow yang telah dipasang.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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 dan Jalankan Kode

Rekayasa Fitur di R

SkillTag.level.intermediateSkillTag.label

4.8+

120 reviews

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!