How does PCA handle correlated features?

Updated May 17, 2026

Short answer

PCA transforms correlated features into uncorrelated principal components.

Deep explanation

Highly correlated features contribute redundant information. PCA rotates feature space to new orthogonal axes where correlation is removed, reducing redundancy.

Real-world example

Combining height and weight correlations in health data.

Common mistakes

Assuming PCA removes features instead of transforming them.

Follow-up questions

Does PCA remove correlation completely?
Why is decorrelation useful?

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