[{'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:print("Now that you\'ve seen some examples, let\'s talk about performance \\n")\nprint("-- Recommender evaluator -- \\n\\n")\nrec_method = input("Which recommendation method do you want to evaluate? \\n(type \'User-based\', \'Item-based\', \'Hybrid\'): ")\nif rec_method == \'User-based\' or rec_method == \'Hybrid\':\nn = int(input("How many similar users to base rec\'s off of? (type an integer)\\n"))\neval_method = input("How do you want to evaluate the recommender system? \\n(type \'MAE\' or \'Recall\')")\nnum_recs = int(input("How many recommendations to give?(type an integer)\\n"))\nprint(\'\\n\')\nif rec_method == "User-based":\nif eval_method == \'MAE\':\nour_funcs.calc_mae_user(nonan_test, 0.01, n, user_sim_mat)\nelif eval_method == \'Recall\':\nour_funcs.user_based_recall(nonan_test, 0.001, n, num_recs, unique_products, metadata, user_sim_mat)\nelif rec_method == \'Hybrid\':\nif eval_method == \'MAE\':\nour_funcs.calc_mae_hyb(nonan_test, 0.01, n, unique_products, metadata, user_sim_mat)\nelif eval_method == \'Recall\':\nour_funcs.hybrid_recall(nonan_test, 0.01, n, num_recs, unique_products, metadata, user_sim_mat)\nelif rec_method == \'Item-based\':\neval_method = input("How do you want to evaluate the recommender system? \\n(type \'MAE\' or \'Recall\')")\nnum_recs = int(input("How many recommendations to give? \\n(type an integer): "))\nif eval_method == \'MAE\':\nour_funcs.calc_mae_itm(nonan_test, 0.01, n, itm_sim_mat)\nelif eval_method == \'Recall\':\nour_funcs.item_based_recall(nonan_test, u1, 0.01, n, num_recs, unique_products, metadata, itm_sim_mat)'}, {'role': 'assistant', 'content': 'evaluation'}]