# 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))
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