Membangun model tereduksi

Analisis tingkat kepentingan variabel membantu Anda mengidentifikasi fitur yang paling prediktif dari himpunan data attrition. Berdasarkan hal tersebut, Anda akan membangun model yang sangat tereduksi hanya dengan tiga variabel: OverTime, DistanceFromHome, dan NumCompaniesWorked lalu membandingkan kinerjanya dengan model penuh sebagai tolok ukur. Metrik yang Anda estimasi untuk model penuh disimpan dalam aug_full.

Seluruh data, beserta pembagian train dan test, tersedia di lingkungan Anda.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

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Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create a recipe using the formula syntax that includes only OverTime, DistanceFromHome and NumCompaniesWorked as predictors
recipe_reduced <-
  ___(Attrition ~ ___ + ___ + ___, data = train)

# Bundle the recipe with your model
workflow_reduced <-
  workflow() %>%
  add_model(model) %>%
  ___

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: Mengurangi fitur model Exercise 2: Menelusuri kepentingan variabel Exercise 3: Menilai kinerja model menggunakan semua prediktor yang tersedia Exercise 4: Membangun model tereduksi

Latihan Saat Ini

Exercise 5: Metode shrinkage Exercise 6: Regularisasi manual dengan Lasso Exercise 7: Menyetel penalty Exercise 8: Finalisasi model Exercise 9: Menggabungkan semuanya Exercise 10: Siapkan dan bagi Exercise 11: Praproses Exercise 12: Model Exercise 13: Evaluasi Exercise 14: Selamat!