Linear Regression, Ridge Regression, Penalty, Loss, Cost Function.
resulting in more stable and robust predictions. In Ridge Regression, the goal is to find the best-fit line or hyperplane that minimizes the sum of squared residuals between the predicted and actual values of the response variable. However, in addition to this objective, Ridge Regression also aims