How does PCA impact interpretability of machine learning models?

PCA reduces interpretability because transformed components are linear combinations of original features.

Each principal component is a weighted combination of all original variables, making it difficult to assign semantic meaning. While PCA improves performance in some models, it sacrifices transparency, especially in regulated domains like finance or healthcare.