step_poly()

Sekarang setelah Anda memiliki baseline, Anda dapat membandingkan kinerja model jika Anda menambahkan transformasi polinom pada semua nilai numerik.

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

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Tambahkan transformasi polinom ke semua prediktor numerik.
Latih workflow pada data train.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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

# Add a polynomial transformation to all numeric predictors
  ___

lr_workflow_poly <- workflow() %>%
  add_model(lr_model) %>%
  add_recipe(lr_recipe_poly)

# Fit workflow to the train data
lr_fit_poly <- ___ %>% fit(train)
lr_aug_poly <- lr_fit_poly %>% augment(test)
lr_aug_poly %>% 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!