Mengimputasi nilai hilang dan membuat variabel dummy

Setelah mendeteksi nilai hilang dalam himpunan data attrition dan menentukan bahwa nilainya hilang sepenuhnya secara acak (MCAR), Anda memutuskan menggunakan imputasi K Nearest Neighbors (KNN). Saat mengonfigurasi recipe rekayasa fitur, Anda memutuskan untuk membuat variabel dummy untuk semua variabel nominal dan memperbarui peran variabel ...1 menjadi "ID" agar dapat Anda pertahankan dalam himpunan data sebagai referensi tanpa memengaruhi model Anda.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

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Petunjuk latihan

Perbarui peran ...1 menjadi "ID".
Imputasikan nilai pada semua prediktor yang memiliki data hilang.
Buat variabel dummy untuk semua prediktor nominal.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

lr_model <- logistic_reg()

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

# Update the role of "...1" to "ID"
  ___(...1, new_role = "ID" ) %>%

# Impute values to all predictors where data are missing
  step_impute_knn(___) %>%

# Create dummy variables for all nominal predictors
  ___(all_nominal_predictors())

lr_recipe

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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!