[{'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:def rmse(y_true, y_pred):\n    return np.sqrt(mean_squared_error(y_true, y_pred))\n\ndef evaluate(df):\n    error = 0\n    for col in TARGETS:\n        error += rmse(df[col].values, df["pred_{}".format(col)].values)\n    return np.round(error/len(TARGETS), 5)\n\n\ndef predict(test_df, first_day, num_days, val=False):\n\n    y_pred = np.clip(model.predict(test_df.loc[test_df["Date"] == first_day][features]), None, 16)\n\n    for i, col in enumerate(TARGETS):\n        test_df["pred_{}".format(col)] = 0\n        test_df.loc[test_df["Date"] == first_day, "pred_{}".format(col)] = y_pred[:, i]\n\n    if val:\n        print(first_day, evaluate(test_df[test_df["Date"] == first_day]))\n\n    for d in range(1, num_days):\n        y_pred = np.clip(model.predict(y_pred), None, 16)\n        date = first_day + timedelta(days=d)\n\n        for i, col in enumerate(TARGETS):\n            test_df.loc[test_df["Date"] == date, "pred_{}".format(col)] = y_pred[:, i]\n\n        if val:\n            print(date, evaluate(test_df[test_df["Date"] == date]))\n        \n    return test_df\n\ntest_df = predict(test_df, TEST_FIRST, TEST_DAYS, val=True)\nevaluate(test_df)'}, {'role': 'assistant', 'content': 'modelling'}]