What is Principal Component Analysis (PCA) and how does it work in feature engineering?
Updated May 16, 2026
Short answer
PCA is a dimensionality reduction technique that transforms correlated features into uncorrelated principal components.
Deep explanation
PCA works by identifying directions (principal components) that maximize variance in the data. It projects data onto a lower-dimensional space while preserving as much information as possible. It is widely used to reduce noise, remove redundancy, and speed up machine learning models.
Real-world example
Used in image compression where thousands of pixel features are reduced to key components.
Common mistakes
- Assuming PCA preserves interpretability of original features.
Follow-up questions
- Does PCA work on categorical data?
- What is explained variance in PCA?