Regularized linear regression is an extension of linear regression that includes a penalty term in the cost function to prevent overfitting. The most common types of regularization are Ridge Regression (L2 regularization) and Lasso Regression (L1 regularization). These methods help to improve the generalization of the model by discouraging overly complex models.
