[{'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 costs\ndef train_test_split_demo(x, y, degree, ratio, seed):\n"""polynomial regression with different split ratios and different degrees."""\ny_train, x_train, y_test, x_test = split_data(x, y, ratio, seed)\nx_train = build_poly(x_train, degree)\nx_test = build_poly(x_test, degree)\nrmse_tr, w = least_squares(y_train, x_train)\nrmse_te = np.sqrt(costs.compute_loss(y_test, x_test, w))\nprint("proportion={p}, degree={d}, Training RMSE={tr:.3f}, Testing RMSE={te:.3f}".format(\np=ratio, d=degree, tr=rmse_tr, te=rmse_te))\nseed = 6\ndegrees = [1, 3, 7, 12]\nsplit_ratios = [0.9, 0.5, 0.1]\nfor split_ratio in split_ratios:\nfor degree in degrees:\ntrain_test_split_demo(x, y, degree, split_ratio, seed)'}, {'role': 'assistant', 'content': "['evaluation']"}]