Menyiapkan panggung

Anda akan mengeksplorasi himpunan data attrition_num dari sudut pandang PCA untuk memahami apakah memungkinkan mengurangi dimensi sambil mempertahankan sebagian besar informasi. Mulailah dengan membuat sebuah recipe yang menyaring fitur dengan varians nyaris nol, menormalisasi data, dan menerapkan PCA.

Himpunan data attrition_num sudah dimuat untuk Anda.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Hapus kemungkinan fitur dengan varians nyaris nol.
Normalisasi semua data numerik.
Terapkan PCA.
Akses nama elemen keluaran dengan menyiapkan recipe.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

pc_recipe <- recipe(~., data = attrition_num) %>%

# Remove possible near-zero variance features
  ___(all_numeric()) %>%

# Normalize all numeric data
  ___(___()) %>%

# Apply PCA
  ___(all_numeric())

# Access the names of the output elements by preparing the recipe
pca_output <- ___(___)
names(pca_output)

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!