How would you describe PCA and what drawbacks might it have?

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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.

How would you describe PCA and what drawbacks might it have?

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