[{'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:df_A = add_features(df, 'A', inplace=False)[['URL_distance_token_sort_ratio', 'URL_distance_levenshtein', 'URL_distance_jaro_winkler', 'Text-length']]\ndf_B = add_features(df, 'B', inplace=False)[['URL_distance_token_sort_ratio', 'URL_distance_levenshtein', 'URL_distance_jaro_winkler', 'Text-length']]\n\ny_pred_A = lr.predict(df_A).reshape(-1, 1)\ny_pred_B = lr.predict(df_B).reshape(-1, 1)\nall_zero = np.zeros_like(y_pred_A)\n\ny_pred = np.hstack((y_pred_A,\n                    y_pred_B,\n                    all_zero\n                   ))\n\nresult = pd.DataFrame(y_pred, index=df.index, columns=['A', 'B', 'NEITHER'])\n\nresult.loc[((result['A'] - result['B']).abs() < 0.1) & (result['A'] < .3), 'NEITHER'] = .3\n\nresult.to_csv('lr_over_URL_similarity.csv')"}, {'role': 'assistant', 'content': 'modelling'}]