Wie blijft er?

Tijd om meerdere transformaties te combineren op de gegevens in attrition_num. Normaliseer of maak numerieke variabelen eerst bijna-normaal met een Yeo-Johnson-transformatie. Zet daarna numerieke voorspellers om naar percentielen, maak dummyvariabelen en verwijder features met een (bijna) nulvariantie.

Deze oefening maakt deel uit van de cursus

Feature engineering in R

Cursus bekijken

Oefeninstructies

Pas een Yeo-Johnson-transformatie toe op alle numerieke variabelen.
Zet alle numerieke voorspellers om naar percentielen.
Maak dummyvariabelen voor alle nominale voorspellers.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

lr_recipe <- recipe(Attrition ~., data = train) %>%

# Apply a Yeo-Johnson transformation to all numeric variables
  ___ %>%

# Transform all numeric predictors into percentiles
 ___ %>%

# Create dummy variables for all nominal predictors
  ___

lr_workflow <- workflow() %>% add_model(lr_model) %>% add_recipe(lr_recipe)
lr_fit <- lr_workflow %>% fit(train)
lr_aug <- lr_fit %>% augment(test)
lr_aug %>% 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!