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AI & Machine Learning · Intermediate

Principal Component Analysis (PCA)

A linear technique that projects data onto orthogonal directions of maximum variance to reduce dimensionality.

Definition

PCA computes principal components, which are uncorrelated linear combinations of the original features ordered by how much variance they explain. Keeping the top components retains most information in fewer dimensions. Features should be standardized first because PCA is sensitive to scale.

Example

Reducing 50 correlated financial ratios to 8 components that explain 95% of variance.

Exam tip

Remember to standardize before PCA, and that components are hard to interpret in business terms.

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