[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:dtrain = xgb.DMatrix(x_train_total, y_train_total)\ndtest = xgb.DMatrix(x_test)\n# specify parameters via map\nparams = {"objective": "reg:linear", # for linear regression\n          "booster" : "gbtree",   # use tree based models \n          "eta": 0.03,   # learning rate\n          "max_depth": 10,    # maximum depth of a tree\n          "subsample": 0.9,    # Subsample ratio of the training instances\n          "colsample_bytree": 0.7,   # Subsample ratio of columns when constructing each tree\n          "silent": 1,   # silent mode\n          "seed": 10   # Random number seed\n          }\nnum_round = 3000\nmodel = xgb.train(params, dtrain, num_round)\n# make prediction\npreds = model.predict(dtest)'}, {'role': 'assistant', 'content': "['modelling']"}]