Normalizing and log-transforming

You are handed a dataset, attrition_num with numerical data about employees who left the company. Features include Age, DistanceFromHome, and MonthlyRate.

You want to use this data to build a model that can predict if an employee is likely to stay, denoted by Attrition, a binary variable coded as a factor. In preparation for modeling, you want to reduce possible skewness and prevent some variables from outweighing others due to variations in scale.

The attrition_numdata 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

Normalize all numeric predictors.
Log-transform all numeric features, with an offset of one.

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) %>%

# Normalize all numeric predictors
  ___(all_numeric_predictors()) %>%

# Log-transform all numeric features, with an offset of one
  ___(___, offset = ___)

lr_workflow <- 
  workflow() %>%
  add_model(lr_model) %>%
  add_recipe(lr_recipe)

lr_workflow

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!