Principal component analysis (PCA) is an essential algorithm in machine learning. It is a mathematical method for evaluating the principal components of a dataset. The principal components are a set of vectors in high-dimensional space that capture the variance (i.e., spread) or variability of the feature space.
The goal of computing principal components is to find a low-dimensional feature sub-space that captures as much information as possible from the original higher-dimensional features of the dataset.
PCA is particularly ...