Normalisasi dan transformasi log

Anda menerima sebuah himpunan data, attrition_num, berisi data numerik tentang karyawan yang keluar dari perusahaan. Fitur mencakup Age, DistanceFromHome, dan MonthlyRate.

Anda ingin menggunakan data ini untuk membangun model yang dapat memprediksi apakah seorang karyawan kemungkinan akan bertahan, ditandai oleh Attrition, sebuah variabel biner yang dikodekan sebagai factor. Sebagai persiapan pemodelan, Anda ingin mengurangi potensi kecondongan (skewness) dan mencegah beberapa variabel mendominasi yang lain karena perbedaan skala.

Data attrition_num serta pembagian train dan test telah dimuat untuk Anda.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Normalisasi semua prediktor numerik.
Terapkan transformasi log pada semua fitur numerik, dengan offset satu.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

lr_model <- logistic_reg()

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

# Normalize all numeric predictors
  ___(all_numeric_predictors()) %>%

# Log-transform all numeric features, with an offset of one
  ___(___, offset = ___)

lr_workflow <- 
  workflow() %>%
  add_model(lr_model) %>%
  add_recipe(lr_recipe)

lr_workflow

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!