The RandomizedSearchCV Object

Just like the GridSearchCV library from Scikit Learn, RandomizedSearchCV provides many useful features to assist with efficiently undertaking a random search. You're going to create a RandomizedSearchCV object, making the small adjustment needed from the GridSearchCV object.

The desired options are:

A default Gradient Boosting Classifier Estimator
5-fold cross validation
Use accuracy to score the models
Use 4 cores for processing in parallel
Ensure you refit the best model and return training scores
Randomly sample 10 models

The hyperparameter grid should be for learning_rate (150 values between 0.1 and 2) and min_samples_leaf (all values between 20 and 65).

You will have available X_train & y_train datasets.

Create a parameter grid as specified in the context above.
Create a RandomizedSearchCV object as outlined in the context above.
Fit the RandomizedSearchCV object to the training data.
Print the values chosen by the modeling process for both hyperparameters.

Take Hint (-30 XP)

Exercise

The RandomizedSearchCV Object

Instructions