gridsearchcv.py

def grid_search_rf(parameter_grid, train_features, train_labels):
    """
    Perform Grid Search on the random forest classifier model, in order to optimize model 
    parameters
    parameter_grid: grid parameters to test against to determine optimal parameters
    train_features: Numpy array, containing training set features
    train_labels: Numpy array, containing training set labels
    """
    # Create a random forest classifier model
    rf = RandomForestClassifier()
    # Instantiate the grid search model
    grid_search = GridSearchCV(estimator = rf, 
                               param_grid = parameter_grid, 
                               cv = 3, 
                               n_jobs = -1, 
                               verbose = 2)
    grid_search.fit(train_features, train_labels)
    print(grid_search.best_params_)

##Execute in main script

#Create the parameter grid, which is plugged into GridSearchCV, where all hyperparameter combos 
#are tested to find the optimal parameters combination
parameter_grid={'max_depth': [80, 90, 100, 110],
                    'n_estimators': [700, 800, 900, 1000, 1100, 1200]}   
grid_search_rf(parameter_grid, train_features, train_labels)

"""
    Grid Search Outputs:
        Fitting 3 folds for each of 20 candidates, totalling 60 fits
        [Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
        [Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  2.1min
        [Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:  3.7min finished
        {'max_depth': 80, 'n_estimators': 1000}
"""