[{'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:import lightgbm as lgb\nX_t, X_e, y_t, y_e = train_test_split(X_train, y_train[:,2], test_size=0.15)\nprint(y_t.shape)\nprint(y_e.shape)\nlgb_train = lgb.Dataset(X_t, y_t)\nlgb_eval = lgb.Dataset(X_e, y_e, reference=lgb_train)\n\nparams = {\n    'objective': 'regression',\n    'metric': 'l2_root',\n    'num_leaves': 43,\n    'max_depth': 16\n\n}\n\ngbm = lgb.train(params,\n                lgb_train,\n                valid_sets=lgb_eval,\n                verbose_eval=0,\n                early_stopping_rounds=5\n               )\n\ny_pred = gbm.predict(X_test)\ny_pred[y_pred < 0] = 1\n\nrmsle(y_pred, y_test[:,2])"}, {'role': 'assistant', 'content': "['evaluation', 'prediction']"}]