[{'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_xgb = {\n    \'learning_rate\': [.1, .5, .7, .9, .95, .99, 1],\n    \'colsample_bytree\': [.3, .4, .5, .6],\n    \'max_depth\': [4],\n    \'alpha\': [3],\n    \'subsample\': [.5],\n    \'n_estimators\': [30, 70, 100, 200]\n}\n\nxgb_model = XGBRegressor()\nxgb_regressor = GridSearchCV(xgb_model, params_xgb, scoring=\'neg_root_mean_squared_error\', cv = 10, n_jobs = -1)\nxgb_regressor.fit(X_train, y_train)\nprint(f\'Optimal lr: {xgb_regressor.best_params_["learning_rate"]}\')\nprint(f\'Optimal colsample_bytree: {xgb_regressor.best_params_["colsample_bytree"]}\')\nprint(f\'Optimal n_estimators: {xgb_regressor.best_params_["n_estimators"]}\')\nprint(f\'Best score: {xgb_regressor.best_score_}\')\''}, {'role': 'assistant', 'content': "['modelling']"}]