What’s your understanding of PCA and its inherent disadvantages?

Question

Anonymous · Accepted Answer

PCA, or principal component analysis, is a process to determine what are the
most important features. I like to think about it in terms of eigenvalues. If
you have a diagonal matrix, then the eigenvalues are the diagonal terms, and the
eigenvectors are the basis vectors. In PCA, the principal components and their
effects aren't as visible, but it is a process to determine them. If I recall
correctly, PCA will return the absolute value of the eigenvalues, rather than
the eigenvalue itself.

What’s your understanding of PCA and its inherent disadvantages?

Did you come across this question in an interview?

Answers