Is PCA a Last Resort?

Eigenvalues and the eigenvectors are very informative they tell you the singularities that can be in your system. For us as data scientists and machine learning engineers where PCA becomes super important is to figure out, well now that I want, I want to emphasize being in PCA space is not the same as being in the original feature space. And so, it's a totally different perspective. It gets rid of all the co linearity, but it also allows you to do this fancy thing that's actually quite simple called parsimony.