Percentuale di varianza spiegata

Da pca_output, puoi recuperare la deviazione standard spiegata da ciascuna componente principale. Poi usa questi valori per calcolare la varianza spiegata e la varianza cumulativa spiegata, e unisci tutto in un tibble.

L'oggetto pca_output è già caricato per te.

Questo esercizio fa parte del corso

Feature Engineering in R

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Istruzioni dell'esercizio

Calcola la percentuale di varianza spiegata usando il vettore delle deviazioni standard.
Crea un tibble con componenti principali, varianza spiegata e varianza cumulativa spiegata.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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 = ___)

Modifica ed esegui il codice

Feature Engineering in R

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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!