[{'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:pipelines = []\n\npipelines.append((\'ScaledLR\', Pipeline([(\'Scaler\', StandardScaler()), (\'LR\', LinearRegression())])))\npipelines.append((\'ScaledLASSO\', Pipeline([(\'poly\', PolynomialFeatures()), (\'Scaler\', StandardScaler()), (\'LASSO\', Lasso(random_state=42))])))\npipelines.append((\'ScaledRID\', Pipeline([(\'poly\', PolynomialFeatures()), (\'Scaler\', StandardScaler()), (\'RID\', Ridge(random_state=42))])))\npipelines.append((\'ScaledKNN\', Pipeline([(\'poly\', PolynomialFeatures()), (\'Scaler\', StandardScaler()), (\'KNN\', KNeighborsRegressor(n_neighbors=2))])))\npipelines.append((\'ScaledCART\', Pipeline([(\'poly\', PolynomialFeatures()), (\'Scaler\', StandardScaler()), (\'CART\', DecisionTreeRegressor(random_state=42))])))\npipelines.append((\'ScaledGBM\', Pipeline([(\'poly\', PolynomialFeatures()), (\'Scaler\', StandardScaler()), (\'GBM\', GradientBoostingRegressor(random_state=42))])))\npipelines.append((\'ScaledRFR\', Pipeline([(\'poly\', PolynomialFeatures()), (\'Scaler\', StandardScaler()), (\'RFR\', RandomForestRegressor(random_state=42))])))\npipelines.append((\'ScaledSVR\', Pipeline([(\'poly\', PolynomialFeatures()), (\'Scaler\', StandardScaler()), (\'SVR\', SVR(kernel=\'linear\'))])))\npipelines.append((\'ScaledXGBR\', Pipeline([(\'poly\', PolynomialFeatures()), (\'Scaler\', StandardScaler()), (\'XGBR\', XGBRegressor(random_state=42))])))\n\nresults = []\nnames = []\nfor name, model in pipelines:\n    kfold = KFold(random_state=42)\n    cv_results = -cross_val_score(model, X_train, y_train, cv=kfold, scoring=\'neg_mean_squared_log_error\')\n    results.append(np.sqrt(cv_results))\n    names.append(name)\n    msg = "{}: {} ({})".format(name, cv_results.mean(), cv_results.std())\n    print(msg)\''}, {'role': 'assistant', 'content': 'result_visualization'}]