[{'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:def chisquared(column, values, df = titanic):\nobserved_survival = []\nexpected_survival = []\nfor value in values:\nobserved_survival.append(df[df[column] == value][\'Survived\'].sum())\nexpected_survival.append(df[df[column] == value][\'Survived\'].count() * baseline_survival)\nresult = stats.chisquare(f_obs=observed_survival, f_exp=expected_survival)\nreturn result\ndef print_chisquared(column, result):\nprint("The P value for the chi-squared test comparing survival rates for",column,"is",result.pvalue,".")\ndef calculate_and_print_chisquared(column, values, df = titanic):\nresult = chisquared(column, values,df=df)\nprint_chisquared(column, result)\ndef pairwise_chisquared(column, values, df = titanic):\ncomparisons = []\nfor pair in combinations(values,2):\nobserved_survival = [df[df[column] == pair[0]][\'Survived\'].sum(), \\\ndf[df[column] == pair[1]][\'Survived\'].sum()]\nexpected_survival = [df[df[column] == pair[0]][\'Survived\'].count() * baseline_survival, \\\ndf[df[column] == pair[1]][\'Survived\'].count() * baseline_survival]\nchisquare_result = stats.chisquare(f_obs=observed_survival, f_exp=expected_survival)\ncomparisons.append((pair, chisquare_result))\nreturn comparisons\ndef print_pairwise_chisquared(column, comparisons):\nfor comparison in comparisons:\nprint("The P value for the chi-squared test comparing survival rates for values",comparison[0][0],"and",comparison[0][1],"for",column,"is",comparison[1].pvalue,".")\nprint("The alpha level for these tests is",alpha/len(comparisons),".")\ndef calculate_and_print_pairwise_chisquared(column, values, df = titanic):\nresult = pairwise_chisquared(column, values,df=df)\nprint_pairwise_chisquared(column, result)'}, {'role': 'assistant', 'content': 'modelling'}]