step_poly()

Artık bir başlangıç (baseline) olduğuna göre, tüm sayısal değerlere polinom dönüşümü eklediğinde modelinin performansını karşılaştırabilirsin.

attrition_num verileri, lojistik regresyon lr_model, kullanıcı tanımlı class-evaluate() fonksiyonu ve train ile test ayrımları senin için zaten yüklendi.

Bu egzersiz

R'da Feature Engineering

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Tüm sayısal yordayıcılara bir polinom dönüşümü ekle.
İş akışını train verisine uydur.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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

# Add a polynomial transformation to all numeric predictors
  ___

lr_workflow_poly <- workflow() %>%
  add_model(lr_model) %>%
  add_recipe(lr_recipe_poly)

# Fit workflow to the train data
lr_fit_poly <- ___ %>% fit(train)
lr_aug_poly <- lr_fit_poly %>% augment(test)
lr_aug_poly %>% class_evaluate(truth = Attrition, estimate = .pred_class,.pred_No)

Kodu Düzenle ve Çalıştır

R'da Feature Engineering

IntermediárioNível de habilidade

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!