PCA - dimension reduction
In the previous exercise, you worked on a dataset with two variables. During an interview, you are likely to face a bigger dataset.
PCA allows reducing the number of variables without significant loss of informational value.
PCA returns the dataset of the same size as your original dataset. It's up to you, how many variables to keep!
The following parameters of prcomp() reduce dimensions based on:
tol- the standard deviation as percentage of the first component's standard deviation,rank- the maximal number of components.
The letters dataset contains numerical attributes of letters.
This exercise is part of the course
Practicing Statistics Interview Questions in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Perform PCA on letters
pca_letters <- ___(letters)
# Output spread measures of principal components
___(pca_letters)