step_percentile()

In che modo l'applicazione di una trasformazione a percentili alle variabili numeriche potrebbe influire sulle prestazioni del modello? Prova!

I dati attrition_num, il modello di regressione logistica lr_model, la funzione definita dall'utente class-evaluate() e le suddivisioni train e test sono già state caricate per te.

Questo esercizio fa parte del corso

Feature Engineering in R

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Istruzioni dell'esercizio

Applica una trasformazione a percentili a tutti i predittori numerici.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Add percentile tansformation to all numeric predictors
lr_recipe_perc <- 
  recipe(Attrition ~., data = train) %>%
  ___
lr_workflow_perc <-
  workflow() %>%
  add_model(lr_model) %>%
  add_recipe(lr_recipe_perc)
lr_fit_perc <- lr_workflow_perc %>% fit(train)
lr_aug_perc <- lr_fit_perc %>% augment(test)
lr_aug_perc %>% class_evaluate(truth = Attrition,
                 estimate = .pred_class,.pred_No)

Modifica ed esegui il codice

Feature Engineering in R

IntermediárioNível de habilidade

4.8+

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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!