[{'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:predict1 = []\n\nreg1 = LinearRegression().fit(x1_train, y1_train)\npre_reg1 = reg1.predict(x1_test)\n\nreg1_poly = LinearRegression().fit(poly_x1_train, y1_train)\npre_reg1_poly = reg1_poly.predict(poly_x1_test)\n\nrf1 = RandomForestRegressor(n_estimators = 250,min_impurity_decrease = 0.001,\n                            max_depth=60).fit(x1_train, y1_train)\npre_rf1 = rf1.predict(x1_test)\n\nxg1 = xgb.XGBRegressor(alpha = 0, eta = 0.03, n_estimators = 1200, \n                       max_depth = 6).fit(x1_train,y1_train)\npre_xg1 = xg1.predict(x1_test)\n\nfor i in range(pre_reg1.size):\n    if pre_reg1[i]<1:\n        pre_reg1[i] = 1 \n    if pre_reg1_poly[i]<1:\n        pre_reg1_poly[i] = 1\n    if pre_rf1[i]<1:\n        pre_rf1[i] = 1\n    if pre_xg1[i]<1:\n        pre_xg1[i] = 1\n\npredict1.append(pre_reg1)\npredict1.append(pre_reg1_poly)\npredict1.append(pre_rf1)\npredict1.append(pre_xg1)\n\nx1_final = x1_test.copy()\nx1_final['Output'] = y1_test\nx1_final['Linear'] = pre_reg1\nx1_final['Lin_poly'] = pre_reg1_poly\nx1_final['RF'] = pre_rf1\nx1_final['XG'] = pre_xg1\nx1_final['Resid'] = y1_test-pre_reg1\nx1_final['Resid_poly'] = y1_test-pre_reg1_poly\nx1_final['Resid_rf'] = y1_test - pre_rf1\nx1_final['Resid_xg'] = y1_test - pre_xg1\n\nfor prediction in range(len(predict1)):\n    print(models[prediction]+\n          str(np.sqrt(mean_squared_log_error(y1_test,predict1[prediction] ))))"}, {'role': 'assistant', 'content': 'modelling'}]