Percentage verklaarde variantie

Uit de pca_output kun je de standaarddeviatie ophalen die door elk hoofcomponent wordt verklaard. Gebruik deze waarden vervolgens om de verklaarde variantie en de cumulatieve verklaarde variantie te berekenen, en voeg deze waarden samen in een tibble.

Het object pca_output is voor je geladen.

Deze oefening maakt deel uit van de cursus

Feature engineering in R

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Oefeninstructies

Bereken het percentage verklaarde variantie met de standaarddeviatievector.
Maak een tibble met hoofcomponenten, verklaarde variantie en cumulatief verklaarde variantie.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

sdev <- pca_output$steps[[3]]$res$sdev
# Calculate percentage of variance explained
var_explained <- ___

# Create a tibble with principal components, variance explained and cumulative variance explained
PCA = tibble(PC = 1:length(sdev), var_explained = ___, 
       cumulative = ___)

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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!