Hyperparameter tuning with RandomizedSearchCV

As you saw, GridSearchCV can be computationally expensive, especially if you are searching over a large hyperparameter space. In this case, you can use RandomizedSearchCV, which tests a fixed number of hyperparameter settings from specified probability distributions.

Training and test sets from diabetes_df have been pre-loaded for you as X_train. X_test, y_train, and y_test, where the target is "diabetes". A logistic regression model has been created and stored as logreg, as well as a KFold variable stored as kf.

You will define a range of hyperparameters and use RandomizedSearchCV, which has been imported from sklearn.model_selection, to look for optimal hyperparameters from these options.

Create params, adding "l1" and "l2" as penalty values, setting C to a range of 50 float values between 0.1 and 1.0, and class_weight to either "balanced" or a dictionary containing 0:0.8, 1:0.2.
Create the Randomized Search CV object, passing the model and the parameters, and setting cv equal to kf.
Fit logreg_cv to the training data.
Print the model's best parameters and accuracy score.

Exercise

Hyperparameter tuning with RandomizedSearchCV

Instructions