What is feature correlation and why can it harm supervised learning models?

Feature correlation occurs when input variables are strongly related, causing redundancy and instability.

Highly correlated features can inflate variance in linear models and make coefficient interpretation unstable. It can also lead to multicollinearity, where model estimates become unreliable. Techniques like PCA, feature selection, or regularization are used to mitigate this issue.