[{'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 dataset_split(df, label_name, train_size=0.6, validation_size=0.2):\n"""\nsplits the data into training, validation and test sets\n"""\ntrain_set = pd.DataFrame(columns=list(df.columns))\nvalidation_set = pd.DataFrame(columns=list(df.columns))\ntest_set = pd.DataFrame(columns=list(df.columns))\nlabels = df[label_name].unique()\nfor label in labels:\nlabel_df = df.ix[df[label_name] == label]\nlabel_df = label_df.sample(frac=1) #randomize\nsize = len(label_df.index)\nvalidation_start = int(train_size*size)\ntest_start = int((train_size+validation_size)*size)\ntrain_set = pd.concat([train_set, label_df[:validation_start]])\nvalidation_set = pd.concat([validation_set, label_df[validation_start:test_start]])\ntest_set = pd.concat([test_set, label_df[test_start:]])\ntrain_set = train_set.sample(frac=1)\nvalidation_set = validation_set.sample(frac=1)\ntest_set = test_set.sample(frac=1)\nreturn train_set, validation_set, test_set\ntrain_set, validation_set, test_set = dataset_split(data,\'Rating\')\ntrain_set[columns] = train_set[columns].astype(int)\nvalidation_set[columns] = validation_set[columns].astype(int)\ntest_set[columns] = test_set[columns].astype(int)\nprint("Training set size: " + str(len(train_set.index)))\nprint("Validation set size: " + str(len(validation_set.index)))\nprint("Test set size: " + str(len(test_set.index)))'}, {'role': 'assistant', 'content': "['data_exploration', 'data_preprocessing']"}]