Imputing missing values and creating dummy variables

After detecting missing values in the attrition dataset and determining that they are missing completely at random (MCAR), you decide to use K Nearest Neighbors (KNN) imputation. While configuring your feature engineering recipe, you decide to create dummy variables for all your nominal variables and update the role of the ...1 variable to "ID" so you can keep it in the dataset for reference, without affecting your model.

This exercise is part of the course

Feature Engineering in R

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Exercise instructions

Update the role of ...1 to "ID".
Impute values to all predictors where data are missing.
Create dummy variables for all nominal predictors.

Hands-on interactive exercise

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

lr_model <- logistic_reg()

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

# Update the role of "...1" to "ID"
  ___(...1, new_role = "ID" ) %>%

# Impute values to all predictors where data are missing
  step_impute_knn(___) %>%

# Create dummy variables for all nominal predictors
  ___(all_nominal_predictors())

lr_recipe

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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!