[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:from helpers import *\nfrom grid_search import *\nimport datetime\ndef test_your_least_squares():\nheight, weight, gender = load_data_from_ex02(sub_sample=False, add_outlier=False)\nx, mean_x, std_x = standardize(height)\ny, tx = build_model_data(x, weight)\ngrid_w0, grid_w1 = generate_w(num_intervals=100)\ngrid_losses = grid_search(y, tx, grid_w0, grid_w1)\nloss_star, w0_star, w1_star = get_best_parameters(grid_w0, grid_w1, grid_losses)\nloss_ls, w_ls = least_squares(y, tx)\nprint("Grid Search: loss={lo}, w=({w0},{w1})".format(lo=np.sqrt(loss_star),w0=w0_star,w1=w1_star))\nprint("Least Squares: loss={lo}, w=({w0},{w1})".format(lo=loss_ls,w0=w_ls[0],w1=w_ls[1]))\ntest_your_least_squares()'}, {'role': 'assistant', 'content': 'load_data'}]