[{'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:## Creating a list for all combinations models\nvotings = []\n\n##  All models\nvotings.append(("Scaled_all_models",Pipeline([("Scaler",StandardScaler()),\n                                      ("Votings",VotingRegressor([("XGBoostR",XGBR_grid.best_estimator_),\n                                                                  ("AdaBoostR", AdaBoostR_grid.best_estimator_),\n                                                                  ("Ridge",ridge_grid.best_estimator_)\n                                                                 ])                                  \n                                    )])))\n\n\n### Combinations of two estimators\n\n## Combination of RandomForestRegressor with  BaggingRegressor & GradientBoostRegressor\nvotings.append(("Scaled_XGBR_AB",Pipeline([("Scaler",StandardScaler()),\n                                      ("Votings",VotingRegressor([("XGBoostR",XGBR_grid.best_estimator_),\n                                                                  ("AdaBoostR", AdaBoostR_grid.best_estimator_)\n                                                                 ]))])\n               ))\n\nvotings.append(("Scaled_XGBR_R",Pipeline([("Scaler",StandardScaler()),\n                                      ("Votings",VotingRegressor([("XGBoostR",XGBR_grid.best_estimator_),\n                                                                  ("Ridge",ridge_grid.best_estimator_)\n                                                                 ]))])))\n\nvotings.append(("Scaled_AB_R",Pipeline([("Scaler",StandardScaler()),\n                                      ("Votings",VotingRegressor([("AdaBoostR", AdaBoostR_grid.best_estimator_),\n                                                                  ("Ridge",ridge_grid.best_estimator_)\n                                                                 ]))])))'}, {'role': 'assistant', 'content': "['modelling']"}]