[{'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:cut_typeA_0 = (svc_freq_0>=mc_typeA_range[0]) & (svc_freq_0<=mc_typeA_range[1])\ncorrect = sum(cut_typeA_0)\ncut_typeA_1 = (svc_freq_1>=mc_typeA_range[0]) & (svc_freq_1<=mc_typeA_range[1])\nincorrect_1 = sum(cut_typeA_1)\ncut_typeA_2 = (svc_freq_2>=mc_typeA_range[0]) & (svc_freq_2<=mc_typeA_range[1])\nincorrect_2 = sum(cut_typeA_2)\nprint('In the range [{:0.2f},{:0.2f}] MHz SVC labeled:'.format(mc_typeA_range[0],mc_typeA_range[1]))\nprint('\\tMain Carrier Type A = {} tracks'.format(correct))\nprint('\\tMain Carrier Type B = {} tracks'.format(incorrect_1))\nprint('\\tSidebands = {} tracks'.format(incorrect_2))\nprint('\\tCorrect Acceptance: {:0.2f} %\\n'.format(correct/(correct+incorrect_1+incorrect_2)*100))\ncut_typeB_0 = (svc_freq_0>=mc_typeB_range[0]) & (svc_freq_0<=mc_typeB_range[1])\nincorrect_1 = sum(cut_typeB_0)\ncut_typeB_1 = (svc_freq_1>=mc_typeB_range[0]) & (svc_freq_1<=mc_typeB_range[1])\ncorrect = sum(cut_typeB_1)\ncut_typeB_2 = (svc_freq_2>=mc_typeB_range[0]) & (svc_freq_2<=mc_typeB_range[1])\nincorrect_2 = sum(cut_typeB_2)\nprint('In the range [{:0.2f},{:0.2f}] MHz SVC labeled:'.format(mc_typeB_range[0],mc_typeB_range[1]))\nprint('\\tMain Carrier Type A = {} tracks'.format(correct))\nprint('\\tMain Carrier Type B = {} tracks'.format(incorrect_1))\nprint('\\tSidebands = {} tracks'.format(incorrect_2))\nprint('\\tCorrect Acceptance: {:0.2f} %\\n'.format(correct/(correct+incorrect_1+incorrect_2)*100))\ncut_typeB_0 = (svc_freq_0>=up_sb_range[0]) & (svc_freq_0<=up_sb_range[1])\nincorrect_1 = sum(cut_typeB_0)\ncut_typeB_1 = (svc_freq_1>=up_sb_range[0]) & (svc_freq_1<=up_sb_range[1])\nincorrect_2 = sum(cut_typeB_1)\ncut_typeB_2 = (svc_freq_2>=up_sb_range[0]) & (svc_freq_2<=up_sb_range[1])\ncorrect = sum(cut_typeB_2)\nprint('In the range [{:0.2f},{:0.2f}] MHz SVC labeled:'.format(up_sb_range[0],up_sb_range[1]))\nprint('\\tMain Carrier Type A = {} tracks'.format(correct))\nprint('\\tMain Carrier Type B = {} tracks'.format(incorrect_1))\nprint('\\tSidebands = {} tracks'.format(incorrect_2))\nprint('\\tCorrect Acceptance: {:0.2f} %\\n'.format(correct/(correct+incorrect_1+incorrect_2)*100))\ncut_typeB_0 = (svc_freq_0>=low_sb_range[0]) & (svc_freq_0<=low_sb_range[1])\nincorrect_1 = sum(cut_typeB_0)\ncut_typeB_1 = (svc_freq_1>=low_sb_range[0]) & (svc_freq_1<=low_sb_range[1])\nincorrect_2 = sum(cut_typeB_1)\ncut_typeB_2 = (svc_freq_2>=low_sb_range[0]) & (svc_freq_2<=low_sb_range[1])\ncorrect = sum(cut_typeB_2)\nprint('In the range [{:0.2f},{:0.2f}] MHz SVC labeled:'.format(low_sb_range[0],low_sb_range[1]))\nprint('\\tMain Carrier Type A = {} tracks'.format(correct))\nprint('\\tMain Carrier Type B = {} tracks'.format(incorrect_1))\nprint('\\tSidebands = {} tracks'.format(incorrect_2))\nprint('\\tCorrect Acceptance: {:0.2f} %\\n'.format(correct/(correct+incorrect_1+incorrect_2)*100))"}, {'role': 'assistant', 'content': "['modelling']"}]