Ridge Regression
is a linear regression technique that adds a regularization penalty to reduce model complexity and help prevent overfitting, particularly when input features are highly correlated.
What is Ridge Regression?
Ridge Regression uses L2 regularization, which adds a penalty based on the squared values of the model's coefficients. This encourages coefficients to remain smaller while still allowing them to contribute to the prediction. The strength of the penalty is controlled by a regularization parameter.
Why is Ridge Regression Important?
Ridge Regression can improve model stability and generalization when datasets contain many correlated features. By limiting excessively large coefficients, it can reduce sensitivity to noise while retaining all available features in the model.
Common use cases
Ridge Regression is commonly used for predictive modeling, forecasting, financial analysis, risk modeling, and datasets with multicollinearity among input variables.