Hyperparameter Tuning for Random Forests Create an instance of RandomForestClassifer with random_state=1 and n_estimators=100 but specifying no other parameter values. Use GridSearchCV to perform hyperparameter tuning with 5-fold cross validation for your random forest model. Score your models according to accuracy. Tune over the max_depth parameter, considering the following hyperparameter values: 12, 16, 20, 24, 28. Print the best hyperparameter value found by grid search, as well as the best score found. Print additional text to indicate the meaning of these values in the output.
a) What is the purpose of hyperparameter tuning?
b) How many folds are used for cross-validation?
c) Which parameter is being tuned in this scenario?
d) What is the significance of the best hyperparameter value and score in model optimization?
