[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': "Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:predict2 = []\n\nreg2 = LinearRegression().fit(x2_train, y2_train)\npre_reg2 = reg2.predict(x2_test)\n\nreg2_poly = LinearRegression().fit(poly_x2_train, y2_train)\npre_reg2_poly = reg2_poly.predict(poly_x2_test)\n\nrf2 = RandomForestRegressor(n_estimators = 100,min_impurity_decrease = 0.001,\n                            max_depth=40).fit(x2_train, y2_train)\npre_rf2 = rf2.predict(x2_test)\n\nxg2 = xgb.XGBRegressor(alpha = 0, eta = 0.05, n_estimators = 800,\n                       max_depth = 6).fit(x2_train,y2_train)\npre_xg2 = xg2.predict(x2_test)\n\nfor i in range(pre_reg2.size):\n    if pre_reg2[i]<1:\n        pre_reg2[i] = 1 \n    if pre_reg2_poly[i]<1:\n        pre_reg2_poly[i] = 1\n    if pre_rf2[i]<1:\n        pre_rf2[i] = 1\n    if pre_xg2[i]<1:\n        pre_xg2[i] = 1\n\npredict2.append(pre_reg2)\npredict2.append(pre_reg2_poly)\npredict2.append(pre_rf2)\npredict2.append(pre_xg2)\n\nx2_final = x2_test.copy()\nx2_final['Output'] = y2_test\nx2_final['Linear'] = pre_reg2\nx2_final['Lin_poly'] = pre_reg2_poly\nx2_final['RF'] = pre_rf2\nx2_final['XG'] = pre_xg2\nx2_final['Resid'] = y2_test-pre_reg2\nx2_final['Resid_poly'] = y2_test-pre_reg2_poly\nx2_final['Resid_rf'] = y2_test - pre_rf2\nx2_final['Resid_xg'] = y2_test - pre_xg2\n\nfor prediction in range(len(predict2)):\n    print(models[prediction]+\n          str(np.sqrt(mean_squared_log_error(y2_test, predict2[prediction]))))"}, {'role': 'assistant', 'content': "['modelling']"}]