[{'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:# parameters\nparams = {\n    "learning_rate": [.1, .5, .7, .9, .95, .99, 1],\n    "colsample_bytree": [.3, .4, .5, .6],\n    "max_depth": [2, 4],\n    "alpha": [1, 3, 5],\n    "subsample": [.5],\n    "n_estimators": [30, 70, 100, 200],\n    "random_state" : [42]\n}\n\n## XGBoost Regressor\nXGBR =  XGBRegressor()\nXGBR_grid = GridSearchCV(XGBR, params, scoring=\'r2\', cv=5, n_jobs=-1)\nXGBR_grid.fit(X_train, y_train)\n\n## Output\nprint("Best parameters:  {}:".format(XGBR_grid.best_params_))\nprint("Best score: {}".format(XGBR_grid.best_score_))\n\n## Append to list\nbest_estimators.append(["XGBoostR",XGBR_grid.best_estimator_])\''}, {'role': 'assistant', 'content': 'modelling'}]