[{'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:predictors = [x for x in train.columns if x not in [target]]\nxgb3 = XGBRegressor(\nlearning_rate =0.1,\nn_estimators=1000,\nmax_depth=4,\nmin_child_weight=4,\ngamma=0,\nsubsample=1,\ncolsample_bytree=0.6,\nobjective= 'reg:linear',\nscale_pos_weight=1,\nseed=27)\nalg=xgb3\ndtrain=train\ncv_folds=5\nearly_stopping_rounds=50\nxgb_param = alg.get_xgb_params()\nxgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values)\ncvresult = xgb.cv(xgb_param, xgtrain,\nnum_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\nmetrics='rmse', early_stopping_rounds=early_stopping_rounds)#, show_progress=False)\nprint(cvresult.shape[0])\ncvresult.tail()"}, {'role': 'assistant', 'content': 'modelling'}]