[{'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 runXGB1(train_X, train_y, test_X, test_y=None, feature_names=None, seed_val=0, num_rounds=4000):\nparam = {}\nparam[\'objective\'] = \'reg:logistic\'\nparam[\'eta\'] = 0.02\nparam[\'max_depth\'] = 6\nparam[\'silent\'] = 1\nparam[\'num_class\'] = 1\nparam[\'eval_metric\'] = "rmse"\nparam[\'min_child_weight\'] = 1\nparam[\'subsample\'] = 0.7\nparam[\'colsample_bytree\'] = 0.7\nparam[\'seed\'] = seed_val\nparam[\'base_score\'] = train_y.mean()\nnum_rounds = num_rounds\nplst = list(param.items())\nxgtrain = xgb.DMatrix(train_X, label=train_y)\nif test_y is not None:\nxgtest = xgb.DMatrix(test_X, label=test_y)\nwatchlist = [ (xgtrain,\'train\'), (xgtest, \'test\') ]\nmodel = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=50, verbose_eval=10)\nelse:\nxgtest = xgb.DMatrix(test_X)\nmodel = xgb.train(plst, xgtrain, num_rounds)\npred_test_y = model.predict(xgtest, ntree_limit=model.best_ntree_limit)\nreturn pred_test_y, model'}, {'role': 'assistant', 'content': 'modelling'}]