Exercise

Hyperparameter tuning with GridSearchCV

Now you have seen how to perform grid search hyperparameter tuning, you are going to build a lasso regression model with optimal hyperparameters to predict blood glucose levels using the features in the diabetes_df dataset.

X_train, X_test, y_train, and y_test have been preloaded for you. A KFold() object has been created and stored for you as kf, along with a lasso regression model as lasso.

Instructions

100 XP

Import GridSearchCV.
Set up a parameter grid for "alpha", using np.linspace() to create 20 evenly spaced values ranging from 0.00001 to 1.
Call GridSearchCV(), passing lasso, the parameter grid, and setting cv equal to kf.
Fit the grid search object to the training data to perform a cross-validated grid search.