Principal Component Analysis

Principal component analysis is a dimensionality-reduction technique that transforms correlated variables into a smaller set of components that preserve much of the original variation. It is used when teams need models to learn patterns from data and generalize those patterns to new examples rather than relying only on hand-written rules.