Hold-out set in practice II: Regression

Remember lasso and ridge regression from the previous chapter? Lasso used the $L1$ penalty to regularize, while ridge used the $L2$ penalty. There is another type of regularized regression known as the elastic net. In elastic net regularization, the penalty term is a linear combination of the $L1$ and $L2$ penalties:

$$ a * L1 + b * L2 $$

In scikit-learn, this term is represented by the 'l1_ratio' parameter: An 'l1_ratio' of 1 corresponds to an $L1$ penalty, and anything lower is a combination of $L1$ and $L2$.

In this exercise, you will GridSearchCV to tune the 'l1_ratio' of an elastic net model trained on the Gapminder data. As in the previous exercise, use a hold-out set to evaluate your model's performance.

Instructions

Import the following modules:
- ElasticNet from sklearn.linear_model.
- mean_squared_error from sklearn.metrics.
- GridSearchCV and train_test_split from sklearn.model_selection.
Create training and test sets, with 40% of the data used for the test set. Use a random state of 42.
Specify the hyperparameter grid for 'l1_ratio' using l1_space as the grid of values to search over.
Instantiate the ElasticNet regressor.
Use GridSearchCV with 5-fold cross-validation to tune 'l1_ratio' on the training data X_train and y_train. This involves first instantiating the GridSearchCV object with the correct parameters and then fitting it to the training data.
Predict on the test set and compute the $R^2$ and mean squared error.

Take Hint (-30xp)