What is multicollinearity in feature engineering?

Multicollinearity occurs when features are highly correlated, causing instability in linear models.

When two or more features are strongly correlated, models like linear regression struggle to assign independent weights. This leads to unstable coefficients and poor interpretability. Techniques like VIF (Variance Inflation Factor) or PCA help mitigate it.