Hyperparameter tuning with RandomizedSearchCV

GridSearchCV can be computationally expensive, especially if you are searching over a large hyperparameter space and dealing with multiple hyperparameters. A solution to this is to use RandomizedSearchCV, in which not all hyperparameter values are tried out. Instead, a fixed number of hyperparameter settings is sampled from specified probability distributions. You'll practice using RandomizedSearchCV in this exercise and see how this works.

Here, you'll also be introduced to a new model: the Decision Tree. Don't worry about the specifics of how this model works. Just like k-NN, linear regression, and logistic regression, decision trees in scikit-learn have .fit() and .predict() methods that you can use in exactly the same way as before. Decision trees have many parameters that can be tuned, such as max_features, max_depth, and min_samples_leaf: This makes it an ideal use case for RandomizedSearchCV.

As before, the feature array X and target variable array y of the diabetes dataset have been pre-loaded. The hyperparameter settings have been specified for you. Your goal is to use RandomizedSearchCV to find the optimal hyperparameters. Go for it!

Instructions

Import DecisionTreeClassifier from sklearn.tree and RandomizedSearchCV from sklearn.model_selection.
Specify the parameters and distributions to sample from. This has been done for you.
Instantiate a DecisionTreeClassifier.
Use RandomizedSearchCV with 5-fold cross-validation to tune the hyperparameters:
- Inside RandomizedSearchCV(), specify the classifier, parameter distribution, and number of folds to use.
- Use the .fit() method on the RandomizedSearchCV object to fit it to the data X and y.
Print the best parameter and best score obtained from RandomizedSearchCV by accessing the best_params_ and best_score_ attributes of tree_cv.

Take Hint (-30xp)