# Conduct search for best params while running cross-validation (GridSearchCV)
rf = RandomForestClassifier()
parameters = {
    'n_estimators': [2**i for i in range(3, 6)],
    'max_depth': [2, 4, 8, 16, 32, None]
}

cv = GridSearchCV(rf, parameters, cv=3)
cv.fit(train_features, train_labels.values.ravel())

#Print Result

def print_results(results):
    print('BEST PARAMS: {}\n'.format(results.best_params_))

    means = results.cv_results_['mean_test_score']
    stds = results.cv_results_['std_test_score']
    for mean, std, params in zip(means, stds, results.cv_results_['params']):
        print('{} (+/-{}) for {}'.format(round(mean, 3), round(std * 2, 3), params))