Transformasi Box-Cox

Dengan menggunakan himpunan data attrition_num yang berisi seluruh data numerik tentang karyawan yang telah keluar dari perusahaan, Anda ingin membangun model yang dapat memprediksi apakah seorang karyawan kemungkinan akan tetap tinggal, menggunakan Attrition, variabel biner yang dikodekan sebagai factor. Untuk membuat distribusi fitur mendekati normal, Anda akan membuat sebuah recipe yang menerapkan transformasi Box-Cox.

Data attrition_num, model regresi logistik lr_model, fungsi buatan pengguna class-evaluate(), serta pemisahan train dan test sudah dimuat untuk Anda.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Buat sebuah recipe yang menggunakan Box-Cox untuk mentransformasi semua fitur numerik, termasuk target.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create a recipe that uses Box-Cox to transform all numeric features
lr_recipe_BC <- 
  recipe(Attrition ~., data = train) %>%
  ___(___)

lr_workflow_BC <- workflow() %>%
  add_model(lr_model) %>%
  add_recipe(lr_recipe_BC)
lr_fit_BC <- lr_workflow_BC %>%
  fit(train)
lr_aug_BC <-
  lr_fit_BC %>% augment(test)
lr_aug_BC %>% class_evaluate(truth = Attrition,
                 estimate = .pred_class,.pred_No)

Edit dan Jalankan Kode

Rekayasa Fitur di R

SkillTag.level.intermediateSkillTag.label

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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!