Persentase varians yang dijelaskan

Dari pca_output, Anda dapat mengambil simpangan baku yang dijelaskan oleh setiap komponen utama. Lalu, gunakan nilai-nilai ini untuk menghitung varians yang dijelaskan dan varians kumulatif yang dijelaskan, kemudian gabungkan nilai-nilai tersebut menjadi sebuah tibble.

Objek pca_output sudah dimuat untuk Anda.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Hitung persentase varians yang dijelaskan dengan memanfaatkan vektor simpangan baku.
Buat sebuah tibble yang memuat komponen utama, varians yang dijelaskan, dan varians kumulatif yang dijelaskan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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 = ___)

Edit dan Jalankan Kode

Rekayasa Fitur di R

SkillTag.level.intermediateSkillTag.label

4.8+

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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!