Resep dasar

Dengan menggunakan himpunan data attrition_num yang berisi seluruh data numerik tentang karyawan yang telah meninggalkan perusahaan, Anda ingin membangun model yang dapat memprediksi apakah seorang karyawan kemungkinan akan tetap tinggal, menggunakan Attrition, variabel biner yang dikodekan sebagai factor. Untuk memulai, Anda akan mendefinisikan resep dasar yang hanya menetapkan formula model dan data pelatihan, tanpa transformasi lain.

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

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Buat resep dasar yang hanya mendefinisikan formula model.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create a plain recipe defining only the model formula
lr_recipe_plain <- 
  ___(___ ~., ___ = ___)

lr_workflow_plain <- workflow() %>%
  add_model(lr_model) %>%
  add_recipe(lr_recipe_plain)
lr_fit_plain <- lr_workflow_plain %>%
  fit(train)
lr_aug_plain <-
  lr_fit_plain %>% augment(test)
lr_aug_plain %>% class_evaluate(truth = Attrition,
                 estimate = .pred_class,.pred_No)

Edit dan Jalankan Kode

Rekayasa Fitur di R

SkillTag.level.intermediateSkillTag.label

4.8+

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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!