Hold-out set in practice I: Classification

You will now practice evaluating a model with tuned hyperparameters on a hold-out set. The feature array and target variable array from the diabetes dataset have been pre-loaded as X and y.

In addition to \(C\), logistic regression has a 'penalty' hyperparameter which specifies whether to use 'l1' or 'l2' regularization. Your job in this exercise is to create a hold-out set, tune the 'C' and 'penalty' hyperparameters of a logistic regression classifier using GridSearchCV on the training set, and then evaluate its performance against the hold-out set.

Instructions

Create the hyperparameter grid:
- Use the array c_space as the grid of values for 'C'.
- For 'penalty', specify a list consisting of 'l1' and 'l2'.
Instantiate a logistic regression classifier.
Create training and test sets. Use a test_size of 0.4 and random_state of 42. In practice, the test set here will function as the hold-out set.
Tune the hyperparameters on the training set using GridSearchCV with 5-folds. This involves first instantiating the GridSearchCV object with the correct parameters and then fitting it to the training data.
Print the best parameter and best score obtained from GridSearchCV by accessing the best_params_ and best_score_ attributes of logreg_cv.

Take Hint (-30xp)