[{'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:# Best: -0.252369 using {\'max_depth\': 6, \'n_estimators\': 1500}\n\n# Best: -1.051575 using {\'max_depth\': 6, \'n_estimators\': 500} - predict shifts log\n\n# Best: -278.598983 using {\'max_depth\': 10, \'n_estimators\': 500} - predict values\n\n# Best: -1.111758 using {\'max_depth\': 6, \'n_estimators\': 500} - predict shifts log, knowing prev log \n\nimport xgboost as xgb\nfrom sklearn.model_selection import GridSearchCV\n\nif optimize_model:\n\n    model = xgb.XGBRegressor(random_state=42)\n    n_estimators_grid = [500, 1000,1500]\n    max_depth_grid = [6, 8, 10]\n    param_grid = dict(max_depth=max_depth_grid, n_estimators=n_estimators_grid)\n    grid_search = GridSearchCV(model, param_grid, scoring="neg_root_mean_squared_error", n_jobs=-1, cv=[(X[mask_train].index,X[mask_test].index)], verbose=1)\n    grid_result = grid_search.fit(X,np.log1p(y))\n    # summarize results\n    print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))\n    print(grid_result.cv_results_)\''}, {'role': 'assistant', 'content': 'modelling'}]