[{'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:def grid_search():\n    from sklearn.ensemble import GradientBoostingRegressor\n    print ('lets go')\n\n    model = GradientBoostingRegressor()\n    # define the grid of values to search\n    grid = dict()\n    grid['n_estimators'] = [4000]\n    grid['learning_rate'] = [ 0.001, 0.01, 0.1]\n    grid['max_depth'] = [4]\n\n    cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n    # define the grid search procedure\n    grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=cv, scoring='neg_mean_squared_error')\n    grid_search=grid_search.fit(X,Y)\n    best_accuracy=grid_search.best_score_\n    best_parameters=grid_search.best_params_\n    print (best_accuracy)\n    print (best_parameters)\n    \ngrid_search()"}, {'role': 'assistant', 'content': 'modelling'}]