Chi resta?

È il momento di esercitarti a combinare più trasformazioni sui dati attrition_num. Per prima cosa, normalizza o porta quasi alla normalità le variabili numeriche applicando una trasformazione di Yeo-Johnson. Poi, trasforma i predittori numerici in percentili, crea variabili dummy ed elimina le feature con varianza quasi nulla.

Questo esercizio fa parte del corso

Feature Engineering in R

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Istruzioni dell'esercizio

Applica una trasformazione di Yeo-Johnson a tutte le variabili numeriche.
Trasforma tutti i predittori numerici in percentili.
Crea variabili dummy per tutti i predittori nominali.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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)

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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!