[{'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:params = {"objective": "reg:linear", # for linear regression\n          "booster" : "gbtree",   # use tree based models \n          "eta": 0.02,   # learning rate\n          "max_depth": 11,    # 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          \'tree_method\': \'gpu_hist\',\n          }\nnum_boost_round = 800\n\n\ndef rmspe_xg(yhat, y):\n    y = np.expm1(y.get_label())\n    yhat = np.expm1(yhat)\n    return "rmspe", rmspe(y,yhat)\n\nimport xgboost as xg'}, {'role': 'assistant', 'content': "['helper_functions']"}]