[{'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:def decision_boundary(t, k):\n"""Decision boundary of a two-column + Class table."""\nt_su = standardize(t.drop(\'Class\')).with_column(\'Class\', t.column(\'Class\'))\ndecisions = Table(t_su.labels)\nfor x in np.arange(-2, 2.1, 0.1):\nfor y in np.arange(-2, 2.1, 0.1):\npredicted = classify(t_su, make_array(x, y), k)\ndecisions.append([x, y, predicted])\ndecisions.scatter(0, 1, colors=\'Class\', alpha=0.4)\nplots.xlim(-2, 2)\nplots.ylim(-2, 2)\nt_su_0 = t_su.where(\'Class\', 0)\nt_su_1 = t_su.where(\'Class\', 1)\nplots.scatter(t_su_0.column(0), t_su_0.column(1), c=\'darkblue\', edgecolor=\'k\')\nplots.scatter(t_su_1.column(0), t_su_1.column(1), c=\'gold\', edgecolor=\'k\')\ndecision_boundary(kidney, 1)'}, {'role': 'assistant', 'content': "['evaluation', 'modelling', 'prediction']"}]