Masuk ke matriks

Anda telah mengidentifikasi enam nilai berbeda untuk EducationField. Namun, Anda menduga bahwa nilai lain mungkin muncul saat Anda menjalankan model pada data baru. Untuk mengantisipasi hal ini, Anda akan membuat indeks hash dengan 50 term. Paket textrecipes serta pemisahan attrition_train dan attrition_test sudah dimuat.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Tambahkan sebuah langkah pada recipe yang menghasilkan indeks dummy_hash untuk EducationField.
Siapkan recipe tersebut.
Panggang (bake) recipe yang sudah disiapkan.
Gabungkan tabel recipe yang telah dipanggang dan nilai EducationField ke dalam satu tabel, lalu cetak 7 baris pertama, serta kolom ke-1 dan ke-18 hingga ke-20.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

recipe <- recipe(~EducationField, data = attrition_train) %>%
# Add a step to the recipe that generates a dummy_hash index for EducationField
  ___(EducationField, prefix = NULL, signed = FALSE, num_terms = 50L)

# Prepare the recipe
object <- recipe %>%
  ___

# Bake the prepped recipe
baked <- ___(object, new_data = attrition_test)

# Bind the baked recipe table and the EducationField values into one table
bind_cols(___, baked)[1:7,c(1,18:20)]

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!