Visualizzare la varianza spiegata

Ora che hai fatto tutti i calcoli, è sempre utile rappresentare i dati in modo visivo. Creerai un grafico a colonne che mostri la varianza spiegata da ciascuna componente principale.

Il vettore variable_explained che hai creato nell'esercizio precedente è disponibile e il theme_() di ggplot() è impostato su classic.

Questo esercizio fa parte del corso

Feature Engineering in R

Visualizza il corso

Istruzioni dell'esercizio

Usa le informazioni nel tibble della PCA per creare un grafico a colonne della varianza spiegata.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

PCA = tibble(PC = 1:length(sdev), var_explained = var_explained, 
       cumulative = cumsum(var_explained))

# Use the information in the PCA tibble to create a column plot of variance explained
PCA %>% ggplot(aes(x = ___, y = ___)) +
  geom_col(fill = "steelblue") +
  xlab("Principal components") +
  ylab("Variance explained")

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!