Intermediate · Data
Principal component analysis (PCA)
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A dimensionality reduction technique that transforms correlated variables into uncorrelated principal components, prioritizing variance.
Technical Definition
A dimensionality reduction technique that transforms correlated variables into uncorrelated principal components, prioritizing variance.
How it works
Principal Component Analysis (PCA) is a statistical method used to simplify complex datasets by reducing the number of variables (dimensionality). It achieves this by transforming the original variables into a new set of uncorrelated variables, called principal components, ordered by the amount of variance they explain.
Related Concepts
- Feature Extraction — Deriving informative numerical signals from raw data for use as model inputs.
- Dimensionality reduction — The process of reducing the number of features or variables in a dataset while retaining essential information.