Baseline

Continuing with the attrition_num dataset, you will create a baseline with a plain recipe to assess the effects of additional feature engineering steps. 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

Bundle the model and recipe into a workflow.
Augment the fitted workflow to get it ready for assessment.

Hands-on interactive exercise

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

lr_recipe_plain <- recipe(Attrition ~., data = train)

# Bundle the model and recipe
lr_workflow_plain <- workflow() %>%
  ___(lr_model) %>%
  ___(lr_recipe_plain)
lr_fit_plain <- lr_workflow_plain %>%
  fit(train)

# Augment the fit workflow
lr_aug_plain <- lr_fit_plain %>%
  ___(___)
lr_aug_plain %>%
  class_evaluate(truth = Attrition,estimate = .pred_class,
                 .pred_No)

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Feature Engineering in R

IntermediateSkill Level

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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!