[{'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:import lightgbm as lgbm\n\nparams_lgbm = {\n    \'learning_rate\': [.01, .1, .5, .7, .9, .95, .99, 1],\n    \'boosting\': [\'gbdt\'],\n    \'metric\': [\'l1\'],\n    \'feature_fraction\': [.3, .4, .5, 1],\n    \'num_leaves\': [20],\n    \'min_data\': [10],\n    \'max_depth\': [10],\n    \'n_estimators\': [10, 30, 50, 100]\n}\n\nlgb = lgbm.LGBMRegressor()\nlgb_regressor = GridSearchCV(lgb, params_lgbm, scoring=\'neg_root_mean_squared_error\', cv = 10, n_jobs = -1)\nlgb_regressor.fit(X_train, y_train)\nprint(f\'Optimal lr: {lgb_regressor.best_params_["learning_rate"]}\')\nprint(f\'Optimal feature_fraction: {lgb_regressor.best_params_["feature_fraction"]}\')\nprint(f\'Optimal n_estimators: {lgb_regressor.best_params_["n_estimators"]}\')\nprint(f\'Best score: {lgb_regressor.best_score_}\')\''}, {'role': 'assistant', 'content': 'modelling'}]