[{'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:from xgboost import XGBRegressor\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import GridSearchCV\nX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.33,random_state=42)\n'''\nparam_test1 = {\n 'max_depth':range(3,10,2),\n 'min_child_weight':range(1,6,2)\n}\n'''\n'''\nparam_test2={\n    'gamma':[0,0.125,0.25,0.5,0.75,1]\n}\n'''\n'''\nparam_test3={\n    'min_child_weight':[1,2,3,4,5,6,7,8,9]\n}\n'''\n'''\nparam_test4={\n    'learning_rate':[0.1,0.01,0.001]\n}\n'''\nparam_test5={\n    'subsample':[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n}\ngsearch1 = GridSearchCV(estimator = XGBRegressor( learning_rate =0.125, n_estimators=1000, max_depth=9,\n min_child_weight=4, gamma=0.125, subsample=0.8, colsample_bytree=0.8,\n random_state=42), \nparam_grid = param_test5,n_jobs=4,iid=False, cv=5)\ngsearch1.fit(X_train,y_train)\npredicted=gsearch1.predict(X_test)\nprint('Model Score: {}'.format(rmlse(np.exp(predicted),np.exp(y_test))))\nprint(gsearch1.best_params_)"}, {'role': 'assistant', 'content': 'modelling'}]