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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.