[{'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<br><br>params = {"objective": "reg:linear", # for linear regression<br>          "booster" : "gbtree",   # use tree based models <br>          "eta": 0.03,   # learning rate<br>          "max_depth": 10,    # maximum depth of a tree<br>          "subsample": 0.9,    # Subsample ratio of the training instances<br>          "colsample_bytree": 0.7,   # Subsample ratio of columns when constructing each tree<br>          "silent": 1,   # silent mode<br>          "seed": 10   # Random number seed<br>          }<br>num_boost_round = 4000<br><br>dtrain = xgb.DMatrix(x_train, y_train)<br>dvalid = xgb.DMatrix(x_valid, y_valid)<br>watchlist = [(dtrain, \'train\'), (dvalid, \'eval\')]<br># train the xgboost model<br>model = xgb.train(params, dtrain, num_boost_round, evals=watchlist, \\\\<br>  early_stopping_rounds= 100, feval=rmspe_xg, verbose_eval=True)'}, {'role': 'assistant', 'content': 'modelling'}]