Trasformazione Yeo-Johnson

Usando il dataset attrition_num con tutti i dati numerici sui dipendenti che hanno lasciato l'azienda, vuoi costruire un modello in grado di prevedere se un dipendente probabilmente resterà, usando Attrition, una variabile binaria codificata come factor. Per far sì che le variabili si comportino in modo quasi normale, creerai una recipe che applica la trasformazione Yeo-Johnson.

I dati attrition_num, la regressione logistica lr_model, la funzione definita dall'utente class-evaluate() e gli split train e test sono già caricati per te.

Questo esercizio fa parte del corso

Feature Engineering in R

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Istruzioni dell'esercizio

Crea una recipe che usi Yeo-Johnson per trasformare tutte le variabili numeriche, incluso il target.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create a recipe that uses Yeo-Johnson to transform all numeric features
lr_recipe_YJ <- 
  recipe(Attrition ~., data = train) %>%
  ___

lr_workflow_YJ <- workflow() %>%
  add_model(lr_model) %>%
  add_recipe(lr_recipe_YJ)
lr_fit_YJ <- lr_workflow_YJ %>%
  fit(train)
lr_aug_YJ <-
  lr_fit_YJ %>% augment(test)
lr_aug_YJ %>% class_evaluate(truth = Attrition,
                 estimate = .pred_class,.pred_No)

Modifica ed esegui il codice

Feature Engineering in R

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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!