How does dimensionality reduction interact with regularization?

Both reduce model complexity but in different ways: DR reduces features, regularization penalizes weights.

Dimensionality reduction simplifies input space, while regularization constrains model parameters. Together, they reduce overfitting but may also remove complementary information if overused. For example, PCA followed by ridge regression often improves stability in high-dimensional settings.