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It's time to practice combining several transformations to the attrition_num data. First, normalize or near-normalize numeric variables by applying a Yeo-Johnson transformation. Next, transform numeric predictors to percentiles, create dummy variables, and eliminate features with near zero variance.

Cet exercice fait partie du cours

Feature Engineering in R

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Instructions

Apply a Yeo-Johnson transformation to all numeric variables.
Transform all numeric predictors into percentiles.
Create dummy variables for all nominal predictors.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

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

# Apply a Yeo-Johnson transformation to all numeric variables
  ___ %>%

# Transform all numeric predictors into percentiles
 ___ %>%

# Create dummy variables for all nominal predictors
  ___

lr_workflow <- workflow() %>% add_model(lr_model) %>% add_recipe(lr_recipe)
lr_fit <- lr_workflow %>% fit(train)
lr_aug <- lr_fit %>% augment(test)
lr_aug %>% class_evaluate(truth = Attrition, estimate = .pred_class,.pred_No)

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Feature Engineering in R

IntermédiaireNiveau de compétence

4.8+

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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!