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Preprocess

Feature engineering time! You need to build a recipe to take care of non-informative but possibly valuable variables such as observation ID or deal with missing values. This is also an opportunity to transform some predictors. Say, normalize numerical features and create dummy variables for categorical ones.

The attrition dataset and the train and test splits you created in the previous exercise are available in your environment.

Questo esercizio fa parte del corso

Feature Engineering in R

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Istruzioni dell'esercizio

  • Normalize all numeric features.
  • Impute missing values using the knn imputation algorithm.
  • Create dummy variables for all nominal predictors.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

recipe <- recipe(Attrition ~ ., data = train) %>%
  update_role(...1, new_role = "ID") %>%

# Normalize all numeric features
  ___(all_numeric_predictors()) %>% 

# Impute missing values using the knn imputation algorithm
  ___(all_predictors()) %>%

# Create dummy variables for all nominal predictors
  ___(all_nominal_predictors())
 
recipe
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