[{'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:from textacy import similarity\n\ndef add_features(df, re_col, inplace=False):\n    \n    if inplace:\n        df_ = df\n    else:\n        df_ = df.copy()\n    \n    df_['URL_last_part'] = df_['URL'].str.rsplit('/', n=1, expand=True)[1].apply(preprocess_so)\n    \n    df_['URL_distance_jaro_winkler'] = df_.apply(lambda row: similarity.jaro_winkler(row['URL_last_part'], row[re_col]), axis=1)\n    df_['URL_distance_levenshtein'] = df_.apply(lambda row: similarity.levenshtein(row['URL_last_part'], row[re_col]), axis=1)\n    df_['URL_distance_token_sort_ratio'] = df_.apply(lambda row: similarity.token_sort_ratio(row['URL_last_part'], row[re_col]), axis=1)\n    \n    return df_\n    \n\nadd_features(X_df, 'RE', inplace=True)\n\nX_df.sample(5, random_state=800)[['URL_last_part', 'URL']]"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]