Plain recipe

Using the attrition_num dataset with all numerical data about employees who have left the company, you want to build a model that can predict if an employee is likely to stay, using Attrition, a binary variable coded as a factor. To get started, you will define a plain recipe that does nothing other than define the model formula and the training data.

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

This exercise is part of the course

Feature Engineering in R

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Exercise instructions

Create a plain recipe defining only the model formula.

Hands-on interactive exercise

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

# Create a plain recipe defining only the model formula
lr_recipe_plain <- 
  ___(___ ~., ___ = ___)

lr_workflow_plain <- workflow() %>%
  add_model(lr_model) %>%
  add_recipe(lr_recipe_plain)
lr_fit_plain <- lr_workflow_plain %>%
  fit(train)
lr_aug_plain <-
  lr_fit_plain %>% augment(test)
lr_aug_plain %>% class_evaluate(truth = Attrition,
                 estimate = .pred_class,.pred_No)

Edit and Run Code

Feature Engineering in R

IntermediateSkill Level

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!