Percent of variance explained

From the pca_output, you can retrieve the standard deviation explained by each principal component. Then, use these values to compute the variance explained and the cumulative variance explained, and glue together these values into a tibble.

The pca_output object is loaded for you.

Este exercício faz parte do curso

Feature Engineering in R

Ver curso

Instruções do exercício

Calculate percentage of variance explained leveraging the standard deviation vector.
Create a tibble with principal components, variance explained and cumulative variance explained.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

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 = ___)

Editar e executar o código

Feature Engineering in R

IntermediárioNível de habilidade

4.8+

75 reviews

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!