Practical Exercises with Regularization Techniques
In this section, you will have the opportunity to apply their knowledge of regularization techniques through a series of practical exercises. These exercises will involve building, tuning, and evaluating regularized linear models—specifically Ridge, Lasso, and ElasticNet—on different datasets. By engaging in these hands-on activities, you will reinforce their understanding of how regularization affects model performance and learn to implement these techniques effectively in real-world scenarios.
Exercise 1: Implementing Ridge Regression
In the first exercise, we will create a new dataset and fit a Ridge regression model to it. We will then evaluate the model's performance using MSE and R-squared (R2) metrics. Finally, we will visualize the model's predictions on the test set to see how well it fits the data. When applicable, be sure to use random_state=123 and/or np.random.seed(123) to ensure reproducibility.
