[{'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:#Best: -0.211107 using {\'max_depth\': 5, \'n_estimators\': 3000}\n\n#Best: -0.940498 using {\'max_depth\': 4, \'n_estimators\': 400}\n\n#Best: -274.964946 using {\'max_depth\': 12, \'n_estimators\': 500}\n\n#Best: -0.861262 using {\'max_depth\': 2, \'n_estimators\': 200}\n\n\nif optimize_model:\n\n    model = xgb.XGBRegressor(random_state=42)\n    n_estimators_grid = [150,200,250]\n    max_depth_grid = [1,2,3]\n    param_grid = dict(max_depth=max_depth_grid, n_estimators=n_estimators_grid)\n    grid_search = GridSearchCV(model, param_grid, scoring="neg_root_mean_squared_error", n_jobs=-1, cv=[(X[mask_train].index,X[mask_test].index)], verbose=1)\n    grid_result = grid_search.fit(X,np.log1p(y))\n    # summarize results\n    print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))\n    print(grid_result.cv_results_)\''}, {'role': 'assistant', 'content': 'modelling'}]