Menyetel penalty

Yakin bahwa Lasso adalah pendekatan yang masuk akal untuk mengurangi jumlah fitur pada model Anda sambil mempertahankan kinerja yang dapat diterima, Anda ingin menyetel model dengan memilih nilai penalty terbaik. Sebuah recipe dasar beserta pembagian train dan test telah dimuat di lingkungan Anda.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Atur model Anda sehingga penalty disetel secara otomatis.
Konfigurasikan grid penalty dengan 30 levels.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Set up your model so that the penalty is tuned automatically
model_lasso_tuned <- logistic_reg() %>% set_engine("glmnet") %>%
  set_args(mixture = 1, ___ = ___) 
workflow_lasso_tuned <- workflow() %>%
  add_model(model_lasso_tuned) %>%
  add_recipe(recipe)

# Configure a penalty grid with 30 levels
penalty_grid <- grid_regular(penalty(range = c(-3, 1)), ___ = ___)

tune_output <- tune_grid(workflow_lasso_tuned,
  resamples = vfold_cv(train, v = 5),
  metrics = metric_set(roc_auc),grid = penalty_grid)

autoplot(tune_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: Mengurangi fitur model Exercise 2: Menelusuri kepentingan variabel Exercise 3: Menilai kinerja model menggunakan semua prediktor yang tersedia Exercise 4: Membangun model tereduksi Exercise 5: Metode shrinkage Exercise 6: Regularisasi manual dengan Lasso Exercise 7: Menyetel penalty

Latihan Saat Ini

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!