[{'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:# def objective(trial):\n#     params = {'metric': {'rmse'},\n#               'max_depth' : trial.suggest_int('max_depth', 1, 10),\n#               'subsumple' : trial.suggest_uniform('subsumple', 0.0, 1.0),\n#               'subsample_freq' : trial.suggest_int('subsample_freq', 0, 1),\n#               'leaning_rate' : trial.suggest_loguniform('leaning_rate', 1e-5, 1),\n#               'feature_fraction' : trial.suggest_uniform('feature_fraction', 0.0, 1.0),\n#               'lambda_l1' : trial.suggest_uniform('lambda_l1' , 0.0, 1.0),\n#               'lambda_l2' : trial.suggest_uniform('lambda_l2' , 0.0, 1.0)}\n \n#     gbm = lgb.train(params,\n#                     lgb_train,\n#                     valid_sets=(lgb_train, lgb_eval),\n#                     num_boost_round=10000,\n#                     early_stopping_rounds=100,\n#                     verbose_eval=50)\n#     predicted = gbm.predict(X_test)\n#     RMSE = np.sqrt(mean_squared_error(y_test, predicted))\n    \n#     pruning_callback = optuna.integration.LightGBMPruningCallback(trial, 'rmse')\n#     return RMSE"}, {'role': 'assistant', 'content': 'helper_functions'}]