[{'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:fig,ax = plt.subplots(2,1,figsize = (10,10))<br>#logcount = np.log1p(train[\'count\']).kurt()<br>#rootcount = np.sqrt(train[\'count\']).kurt()<br>#cubiccount = np.power(train[\'count\'],2).kurt()<br>#minVal = min([logcount, rootcount, cubiccount])<br>#if logcount == minVal:<br>best = \'log\'<br>train[\'count_log\'] = np.log1p(train[\'count\'])<br>sns.distplot(train[\'count_log\'],ax=ax[0])<br>stats.probplot(train["count_log"], dist=\'norm\', fit=True, plot=ax[1])<br>#elif rootcount == minVal:<br>    #best = \'root\'<br>    #train[\'count_root\'] = np.sqrt(train[\'count\'])<br>    #sns.distplot(train[\'count_root\'],ax=ax[0])<br>    #stats.probplot(train["count_root"], dist=\'norm\', fit=True, plot=ax[1])<br>#elif cubiccount == minVal:<br>    #best = \'cubic\'<br>    #train[\'count_cubic\'] = np.power(train[\'count\'],2)<br>    #sns.distplot(train[\'count_cubic\'],ax=ax[0])<br>    #stats.probplot(train["count_cubic"], dist=\'norm\', fit=True, plot=ax[1])<br>#print(\'For count, the Best TF is \' + best)'}, {'role': 'assistant', 'content': "['result_visualization']"}]