[{'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 pandas import *\nfrom sklearn.feature_selection import *\n\'\'\'\nhttp://scikit-learn.org/stable/datasets/twenty_newsgroups.html\nhttp://scikit-learn.org/stable/modules/feature_selection.html\nhttp://scikit-learn.org/stable/auto_examples/text/document_classification_20newsgroups.html#example-text-document-classification-20newsgroups-py\nThese objects take as input a scoring function that returns univariate p-values:\n-For regression: f_regression\n-For classification: chi2 or f_classif\nFeature selection with sparse data:\n-If you use sparse data (i.e. data represented as sparse matrices),\nonly chi2 will deal with the data without making it dense.\n-Warning: Beware not to use a regression scoring function with a classification problem,\nyou will get useless results.\nWith SVMs and logistic-regression, the parameter C controls the sparsity:\nthe smaller C the less features selected.\n\'\'\'\ndef top20(type):\nif type == "unigram":\nvectorizer = TfidfVectorizer(min_df=1, stop_words=\'english\')\ntrain_vectors = vectorizer.fit_transform(train_text)\nprint\nprint("----------")\nprint("unigram")\nprint("----------")\nprint\nprint("train_vectors.shape:", train_vectors.shape)\nprint\nelif type == "bigram":\nvectorizer = TfidfVectorizer(min_df=1, stop_words=\'english\', ngram_range=(2, 2))\ntrain_vectors = vectorizer.fit_transform(train_text)\nprint\nprint("----------")\nprint("bigram")\nprint("----------")\nprint\nprint("train_vectors.shape:", train_vectors.shape)\nprint\nfor c in [12]:\nlr = LogisticRegression(penalty=\'l2\',C=c)\nlr.fit(train_vectors, train_labels)\nprint("lr.coef_:")\nprint(lr.coef_)\nprint\ntop20 = sorted(range(len(lr.coef_[0])), key=lambda i: abs(lr.coef_[0][i]), reverse=True)[:20]\ncol_1 = []\nfor index in (top20):\ncol_1.append(lr.coef_[0][index])\nprint("top 20:" )\nprint(top20)\nprint\nfeature_names = np.asarray(vectorizer.get_feature_names())\ndf = DataFrame({\'Feature\': feature_names[top20], \'word\': col_1})\nprint(df    )\ntop20("unigram")\ntop20("bigram")'}, {'role': 'assistant', 'content': 'modelling'}]