[{'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:ts = time.time()\n\ndef ridge_reg_basic_all_countries(data, day_start):\n    \n    data2 = data.loc[data.Day_num >= day_start]\n\n    # Set the dataframe where we will update the predictions\n    data_pred = data[data.ForecastId != -1][[\'Country_Region\', \'Province_State\', \'Day_num\', \'ForecastId\']]\n    data_pred = data_pred.loc[data_pred[\'Day_num\']>=day_start]\n    data_pred[\'Predicted_ConfirmedCases\'] = [0]*len(data_pred)\n    data_pred[\'Predicted_Fatalities\'] = [0]*len(data_pred)\n\n    print("Currently running Logistic Regression for all countries")\n\n    # Main loop for countries\n    for c in data2[\'Country_Region\'].unique():\n\n        # List of provinces\n        provinces_list = data2[data2[\'Country_Region\']==c][\'Province_State\'].unique()\n\n        # If the country has several Province/State informed\n        if len(provinces_list)>1:\n            for p in provinces_list:\n                data_cp = data2[(data2[\'Country_Region\']==c) & (data2[\'Province_State\']==p)]\n                X_train, Y_train_1, Y_train_2, X_test = split_data(data_cp)\n                model_1, pred_1 = ridge_reg(X_train, Y_train_1, X_test)\n                model_2, pred_2 = ridge_reg(X_train, Y_train_2, X_test)\n                data_pred.loc[((data_pred[\'Country_Region\']==c) & (data2[\'Province_State\']==p)), \'Predicted_ConfirmedCases\'] = pred_1\n                data_pred.loc[((data_pred[\'Country_Region\']==c) & (data2[\'Province_State\']==p)), \'Predicted_Fatalities\'] = pred_2\n\n        # No Province/State informed\n        else:\n            data_c = data2[(data2[\'Country_Region\']==c)]\n            X_train, Y_train_1, Y_train_2, X_test = split_data(data_c)\n            model_1, pred_1 = ridge_reg(X_train, Y_train_1, X_test)\n            model_2, pred_2 = ridge_reg(X_train, Y_train_2, X_test)\n            data_pred.loc[(data_pred[\'Country_Region\']==c), \'Predicted_ConfirmedCases\'] = pred_1\n            data_pred.loc[(data_pred[\'Country_Region\']==c), \'Predicted_Fatalities\'] = pred_2\n\n    # Apply exponential transf. and clean potential infinites due to final numerical precision\n    data_pred[[\'Predicted_ConfirmedCases\', \'Predicted_Fatalities\']] = data_pred[[\'Predicted_ConfirmedCases\', \'Predicted_Fatalities\']].apply(lambda x: np.expm1(x))\n    data_pred.replace([np.inf, -np.inf], 0, inplace=True) \n    \n    return data_pred\n\n\nday_start = 52\ndata_pred = ridge_reg_basic_all_countries(data, day_start)\nget_submission(\'submission\', data_pred, \'Predicted_ConfirmedCases\', \'Predicted_Fatalities\')\n\nprint("Process finished in ", round(time.time() - ts, 2), " seconds")\''}, {'role': 'assistant', 'content': 'evaluation'}]