[{'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:from sklearn.decomposition import TruncatedSVD\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n\ndef extract_col_interaction(dataset, col1, col2, tfidf = True):\n    data = dataset.groupby([col1])[col2].agg(lambda x: " ".join(list([str(y) for y in x])))\n    if tfidf:\n        vectorizer = TfidfVectorizer(tokenizer=lambda x: x.split(" "))\n    else:\n        vectorizer = CountVectorizer(tokenizer=lambda x: x.split(" "))\n    \n    data_X = vectorizer.fit_transform(data)\n    dim_red = TruncatedSVD(n_components=1, random_state = 5115)\n    data_X = dim_red.fit_transform(data_X)\n    \n    result = pd.DataFrame()\n    result[col1] = data.index.values\n    result[col1+"_{}_svd".format(col2)] = data_X.ravel()\n    return result\n\nimport itertools\ndef get_col_interactions_svd(dataset, tfidf = True):\n    new_dataset = pd.DataFrame()\n    for col1,col2 in itertools.permutations(dataset.columns, 2):\n        data = extract_col_interaction(dataset, col1,col2, tfidf)\n        col_name = [x for x in data.columns if "svd" in x][0]\n        new_dataset[col_name] = dataset[[col1]].merge(data, on = col1, how = \'left\')[col_name]\n    return new_dataset\''}, {'role': 'assistant', 'content': 'data_preprocessing'}]