Siapa yang tetap tinggal?

Saatnya berlatih menggabungkan beberapa transformasi pada data attrition_num. Pertama, normalisasi atau hampir-normalisasi variabel numerik dengan menerapkan transformasi Yeo-Johnson. Selanjutnya, ubah prediktor numerik menjadi persentil, buat variabel dummy, dan hilangkan fitur dengan varians nyaris nol.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Terapkan transformasi Yeo-Johnson pada semua variabel numerik.
Ubah semua prediktor numerik menjadi persentil.
Buat variabel dummy untuk semua prediktor nominal.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

lr_recipe <- recipe(Attrition ~., data = train) %>%

# Apply a Yeo-Johnson transformation to all numeric variables
  ___ %>%

# Transform all numeric predictors into percentiles
 ___ %>%

# Create dummy variables for all nominal predictors
  ___

lr_workflow <- workflow() %>% add_model(lr_model) %>% add_recipe(lr_recipe)
lr_fit <- lr_workflow %>% fit(train)
lr_aug <- lr_fit %>% augment(test)
lr_aug %>% class_evaluate(truth = Attrition, estimate = .pred_class,.pred_No)

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!