How does PCA behave with imbalanced variance across features?

PCA tends to overemphasize high-variance features, potentially ignoring smaller but meaningful signals.

Since PCA maximizes variance, features with large variance dominate the principal components. This can lead to suppression of low-variance but important features. Proper scaling and domain analysis are required to ensure meaningful component extraction.