Practical exercises with regularization techniques
In this section, you will have the opportunity to apply your 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 your understanding of how regularization affects model performance and learn how 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 the MSE and 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...
