Prepping the stage

You are going to explore the attrition_num dataset from the point of view of PCA to understand if it is feasible to reduce dimensionality while preserving most information. Start by creating a recipe that filters our near-zero variance features, normalizes the data, and implements PCA.

The attrition_num dataset is already loaded for you.

This exercise is part of the course

Feature Engineering in R

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Exercise instructions

Remove possible near-zero variance features.
Normalize all numeric data.
Apply PCA.
Access the names of the output elements by preparing the recipe.

Hands-on interactive exercise

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

pc_recipe <- recipe(~., data = attrition_num) %>%

# Remove possible near-zero variance features
  ___(all_numeric()) %>%

# Normalize all numeric data
  ___(___()) %>%

# Apply PCA
  ___(all_numeric())

# Access the names of the output elements by preparing the recipe
pca_output <- ___(___)
names(pca_output)

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!