Description: Ridge regression adds L2 regularization to [[Linear Regression]] to handle multicollinearity and prevent overfitting.
Key Points:
- Shrinks coefficients to reduce overfitting.
- Handles multicollinearity well.
- Requires tuning of the [[Data Science/Regularization]] parameter.
Applications: Economic forecasting, portfolio optimization, marketing analysis.