step_poly()

Now that you have a baseline, you can compare your model's performance if you add a polynomial transformation to all numerical values.

The attrition_numdata, the logistic regression lr_model, the user-defined class-evaluate() function, and the trainand test splits have already been loaded for you.

This exercise is part of the course

Feature Engineering in R

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Exercise instructions

Add a polynomial transformation to all numeric predictors.
Fit workflow to the train data.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

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)

Edit and Run Code

Feature Engineering in R

IntermediateSkill Level

4.9+

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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!