Exercise

# Ridge regularization

In the last exercise you practiced performing lasso regularization. If you're asked about regularization techniques in a machine learning interview, know what differentiates the 2 norms. Lasso uses the L1 norm corresponding to the penalty parameter and the absolute value of the coefficients. Ridge regression performs L2 regularization, also known as L2-norm, which adds a penalty term to ordinary least squares using the penalty parameter and the sum of the squared coefficients.

For this exercise, you'll practice regularization with Ridge on the `diabetes`

DataFrame. The feature matrix and target array are saved to your workspace as `X`

and `y`

, respectively.

Already imported for you are `mean_squared_error`

from `sklearn.metrics`

and `train_test_split`

from `sklearn.model_selection`

.

Instructions 1/4

**undefined XP**

- Import the functions needed for regular and cross-validated Ridge Regression, as well as mean squared error.