Baseline

Verder met de gegevensset attrition_num maak je een baseline met een eenvoudige recipe om de effecten van extra feature engineering-stappen te beoordelen. De attrition_num-data, de logistische regressie lr_model, de door de gebruiker gedefinieerde functie class-evaluate() en de train- en test-splits zijn al voor je geladen.

Deze oefening maakt deel uit van de cursus

Feature engineering in R

Cursus bekijken

Oefeninstructies

Bundel het model en de recipe in een workflow.
Augment de gefitte workflow om deze klaar te maken voor evaluatie.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

lr_recipe_plain <- recipe(Attrition ~., data = train)

# Bundle the model and recipe
lr_workflow_plain <- workflow() %>%
  ___(lr_model) %>%
  ___(lr_recipe_plain)
lr_fit_plain <- lr_workflow_plain %>%
  fit(train)

# Augment the fit workflow
lr_aug_plain <- lr_fit_plain %>%
  ___(___)
lr_aug_plain %>%
  class_evaluate(truth = Attrition,estimate = .pred_class,
                 .pred_No)

Code bewerken en uitvoeren

Feature engineering in R

SkillTag.level.intermediateSkillTag.label

4.8+

120 reviews

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!