[{'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:# Best: -0.211438 using {\'max_depth\': 5, \'n_estimators\': 2500}\n\n# Best: -0.974302 using {\'max_depth\': 5, \'n_estimators\': 400} - predict shifts log\n\n# Best: -274.964946 using {\'max_depth\': 12, \'n_estimators\': 500}\n\n# Best: -1.064197 using {\'max_depth\': 5, \'n_estimators\': 400}\n\nif optimize_model:\n\n    model = xgb.XGBRegressor(random_state=42)\n    n_estimators_grid = [400,500,600]\n    max_depth_grid = [5,6,7]\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']"}]