Ridge Regression

Ridge Regression is a linear regression technique that adds a penalty to the size of the coefficients. This penalty, known as L2 regularization, shrinks the coefficient values towards zero. By adding this constraint, Ridge Regression aims to reduce the model's complexity and prevent overfitting, especially when dealing with datasets that have high multicollinearity (where predictor variables are highly correlated).

Visit the following resources to learn more:

• @article@Ridge | scikit-learn
• @video@Mastering Ridge Regression in Python with scikit-learnv