[{'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:err =[]\nreich_fit_reports = []\nreich_fit_pars = []\nfor i in liste_reich:\nif i[0].size < 1:\ncontinue\nerr_temp = None\nerr_temp = np.sqrt(i[1])\nerr_temp = np.where(err_temp == 0, 1, err_temp)\nerr.append(np.where(np.sqrt(i[1]) == 0, 1, np.sqrt(i[1])))\npars = mod_pos.make_params()\npars['B'].set(vary=False)\nfit = mod_pos.fit(i[1],\npars,\nA= np.sum(i[0]*i[1]/180),\nB=np.sum(i[1]),\nweights=1/err_temp,\nx=i[0])\nreich_fit_reports.append(fit.fit_report())\nreich_fit_pars.append(np.array([fit.best_values,fit.chisqr]))\nfit.plot()\nplt.show()"}, {'role': 'assistant', 'content': "['evaluation', 'modelling']"}]