How does PCA influence feature importance interpretation in ML models?

PCA removes direct feature interpretability because transformed components are linear combinations of all features.

After PCA transformation, original features no longer exist independently. Instead, each principal component is a weighted sum of all features (loadings). This makes traditional feature importance methods like permutation importance or coefficients difficult to interpret. While loadings provide indirect insight, they do not represent isolated feature effects.