GridSearchCV with Scikit Learn

The GridSearchCV module from Scikit Learn provides many useful features to assist with efficiently undertaking a grid search. You will now put your learning into practice by creating a GridSearchCV object with certain parameters.

The desired options are:

A Random Forest Estimator, with the split criterion as 'entropy'
5-fold cross validation
The hyperparameters max_depth (2, 4, 8, 15) and max_features ('auto' vs 'sqrt')
Use roc_auc to score the models
Use 4 cores for processing in parallel
Ensure you refit the best model and return training scores

You will have available X_train, X_test, y_train & y_test datasets.

Create a Random Forest estimator as specified in the context above.
Create a parameter grid as specified in the context above.
Create a GridSearchCV object as outlined in the context above, using the two elements created in the previous two instructions.