Missende waarden imputeren en dummyvariabelen maken

Nadat je missende waarden in de attrition-gegevensset hebt gevonden en hebt vastgesteld dat ze volledig willekeurig ontbreken (MCAR), besluit je K Nearest Neighbors (KNN)-imputatie te gebruiken. Bij het configureren van je feature engineering-recipe besluit je dummyvariabelen te maken voor al je nominale variabelen en de rol van de variabele ...1 bij te werken naar "ID", zodat je die in de gegevensset kunt houden ter referentie zonder je model te beïnvloeden.

Deze oefening maakt deel uit van de cursus

Feature engineering in R

Bekijk cursus

Oefeninstructies

Werk de rol van ...1 bij naar "ID".
Imputeer waarden voor alle predictoren waar gegevens ontbreken.
Maak dummyvariabelen voor alle nominale predictoren.

Interactieve oefening met praktijkervaring

Probeer deze oefening door deze voorbeeldcode aan te vullen.

lr_model <- logistic_reg()

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

# Update the role of "...1" to "ID"
  ___(...1, new_role = "ID" ) %>%

# Impute values to all predictors where data are missing
  step_impute_knn(___) %>%

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

lr_recipe

Code bewerken en uitvoeren

Feature engineering in R

SkillTag.level.intermediateSkillTag.label

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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!