Het podium voorbereiden

Je gaat de gegevensset attrition_num verkennen vanuit het perspectief van PCA om te begrijpen of het haalbaar is om de dimensionaliteit te verlagen terwijl je de meeste informatie behoudt. Begin met het maken van een recipe die features met bijna-nulvariantie wegfiltert, de data normaliseert en PCA toepast.

De gegevensset attrition_num is al voor je geladen.

Deze oefening maakt deel uit van de cursus

Feature engineering in R

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Oefeninstructies

Verwijder mogelijke features met bijna-nulvariantie.
Normaliseer alle numerieke data.
Pas PCA toe.
Raadpleeg de namen van de outputelementen door de recipe voor te bereiden.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

pc_recipe <- recipe(~., data = attrition_num) %>%

# Remove possible near-zero variance features
  ___(all_numeric()) %>%

# Normalize all numeric data
  ___(___()) %>%

# Apply PCA
  ___(all_numeric())

# Access the names of the output elements by preparing the recipe
pca_output <- ___(___)
names(pca_output)

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!