[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:rf_model = RandomForestRegressor(max_depth=rf_regressor.best_params_["max_depth"], \n                                 max_features=rf_regressor.best_params_["max_features"], \n                                 min_samples_leaf=rf_regressor.best_params_["min_samples_leaf"], \n                                 min_samples_split=rf_regressor.best_params_["min_samples_split"], \n                                 n_estimators=rf_regressor.best_params_["n_estimators"], \n                                 n_jobs=-1, oob_score=True)\nrf_model.fit(X_train, y_train)\ny_train_pred = rf_model.predict(X_train)\ny_pred = rf_model.predict(X_test)\nprint(\'Train r2 score: \', r2_score(y_train_pred, y_train))\nprint(\'Test r2 score: \', r2_score(y_test, y_pred))\ntrain_rmse = np.sqrt(mean_squared_error(y_train_pred, y_train))\ntest_rmse = np.sqrt(mean_squared_error(y_test, y_pred))\nprint(f\'Train RMSE: {train_rmse:.4f}\')\nprint(f\'Test RMSE: {test_rmse:.4f}\')\''}, {'role': 'assistant', 'content': 'modelling'}]