In case you’ve been studying about machine studying and knowledge science, you’ve in all probability ran into this scary-looking factor known as PCA — Principal Element Evaluation. It sounds very difficult, nearly like one thing solely math geniuses would perceive.
However don’t fear, at present we’re going to interrupt it down very merely, and I promise: no want to speak about one thing known as “eigenvectors”!
PCA is a method that helps us simplify advanced knowledge whereas conserving as a lot necessary data as attainable.
Think about you’ve got an enormous dataset with dozens, perhaps a whole bunch of options (columns). A few of these options are very related, some is likely to be redundant, and a few simply add noise. PCA helps us scale back all that mess into fewer, extra significant parts.
Consider it like packing a suitcase: you may’t take your whole closet on a visit, so that you choose solely an important garments, those that signify the perfect of what you’ve got.
PCA does the identical along with your knowledge: it retains the essence and discards the remainder.
Good query!
In machine studying, too many options could cause issues.
That is known as the curse of dimensionality (and sure, it sounds dramatic, and in actuality it’s however stick with me).
