How does PCA handle multicollinearity in regression models?

PCA removes multicollinearity by transforming correlated features into orthogonal components.

Multicollinearity inflates variance of regression coefficients. PCA transforms correlated predictors into independent principal components, stabilizing regression models. This is often called Principal Component Regression (PCR).