Linear Regression, Ridge Regression, Penalty, Loss, Cost Function.
multicollinearity: Ridge Regression handles multicollinearity effectively by shrinking the coefficients of highly correlated variables. This helps to stabilize the model and reduce the impact of multicollinearity on coefficient estimates. 2. Preventing overfitting: The regularization term in Ridge