[{'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:# Enconding frequencies instead of labels (so we have some numeric variables)\n\ndef frequency_encoding(column, df, df_test=None):\n    frequencies = df[column].value_counts().reset_index()\n    df_values = df[[column]].merge(frequencies, how='left', \n                                   left_on=column, right_on='index').iloc[:,-1].values\n    if df_test is not None:\n        df_test_values = df_test[[column]].merge(frequencies, how='left', \n                                                 left_on=column, right_on='index').fillna(1).iloc[:,-1].values\n    else:\n        df_test_values = None\n    return df_values, df_test_values\n\nfor column in X.columns:\n    train_values, test_values = frequency_encoding(column, X, Xt)\n    X[column+'_counts'] = train_values\n    Xt[column+'_counts'] = test_values"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]