[{'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 xgboost as xgb\nfrom sklearn.model_selection import train_test_split\n\nX.set_index(\'device_id\', inplace=True)\nX_train, X_val, y_train, y_val = train_test_split(X, Y, train_size=.80)\n\n##################\n#     XGBoost\n##################\n\ndtrain = xgb.DMatrix(X_train, y_train)\ndvalid = xgb.DMatrix(X_val, y_val)\n\nparams = {\n    "objective": "multi:softprob",\n    "num_class": 12, # Y12\n    "booster": "gbtree", # gbtree,gbliner\n    "eval_metric": "mlogloss",\n    "eta": 0.3, # GBM learning rate \n    "silent": 0, # 10\n}\nwatchlist = [(dtrain, \'train\'), (dvalid, \'eval\')]\ngbm = xgb.train(params, dtrain, 140, evals=watchlist, verbose_eval=True)\''}, {'role': 'assistant', 'content': 'modelling'}]