PCA

Principal Component Analysis

Dimensionality-reduction technique using variance-aligned axes.

When you'd see it: Data preparation and exploration, when a dataset has too many correlated features to model or visualize.

Why it matters: PCA compresses many correlated variables into a few new axes that capture most of the variation, making data easier to model and visualize. It is a standard first move for high-dimensional data.

Common mistakes: Interpreting the resulting components as real-world quantities. They are mathematical combinations of features, not directly meaningful measures.