Into the matrix

You identified six distinct values for EducationField. But you suspect that others might show up as you run the model on new data. To prepare for this, you will create a hash index with 50 terms. The textrecipes package, the attrition_train, and the attrition_test splits are already loaded.

This exercise is part of the course

Feature Engineering in R

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Exercise instructions

Add a step to the recipe that generates a dummy_hash index for EducationField.
Prepare the recipe.
Bake the prepared recipe.
Bind the baked recipe table and the EducationField values into one table and print the first 7 rows, as well as columns 1 and 18 to 20.

Hands-on interactive exercise

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

recipe <- recipe(~EducationField, data = attrition_train) %>%
# Add a step to the recipe that generates a dummy_hash index for EducationField
  ___(EducationField, prefix = NULL, signed = FALSE, num_terms = 50L)

# Prepare the recipe
object <- recipe %>%
  ___

# Bake the prepped recipe
baked <- ___(object, new_data = attrition_test)

# Bind the baked recipe table and the EducationField values into one table
bind_cols(___, baked)[1:7,c(1,18:20)]

Edit and Run Code

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!