De matrix in

Je hebt zes verschillende waarden voor EducationField geïdentificeerd. Maar je vermoedt dat er meer kunnen opduiken wanneer je het model op nieuwe data draait. Om je hierop voor te bereiden, maak je een hash-index met 50 termen. Het pakket textrecipes en de splits attrition_train en attrition_test zijn al geladen.

Deze oefening maakt deel uit van de cursus

Feature engineering in R

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Oefeninstructies

Voeg een stap toe aan de recipe die een dummy_hash-index genereert voor EducationField.
Prepare de recipe.
Bake de geprepte recipe.
Bind de gebakte recipe-tabel en de waarden van EducationField samen tot één tabel en print de eerste 7 rijen, evenals kolommen 1 en 18 tot en met 20.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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)]

Code bewerken en uitvoeren

Feature engineering in R

SkillTag.level.intermediateSkillTag.label

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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!