[{'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:import numpy as np\nimport pandas as pd\nimport gc\nimport xgboost as xgb\nimport math\nfrom sklearn.cross_validation import train_test_split\nfrom ml_metrics import rmsle\ndef evalerror(preds, dtrain):\n\n    labels = dtrain.get_label()\n    assert len(preds) == len(labels)\n    labels = labels.tolist()\n    preds = preds.tolist()\n    terms_to_sum = [(math.log(labels[i] + 1) - math.log(max(0,preds[i]) + 1)) ** 2.0 for i,pred in enumerate(labels)]\n    return \'error\', (sum(terms_to_sum) * (1.0/len(preds))) ** 0.5\n\nnrows=10000\ntrain=pd.read_csv(\'../input/train.csv\',nrows=nrows)\ntest=pd.read_csv(\'../input/test.csv\',nrows=nrows)\n\nprint(train.columns)\nprint(test.columns)\nprint(test.columns.values)\nids=test[\'id\']\ntest=test.drop([\'id\'],axis=1)\ny=train[\'Demanda_uni_equil\']\nX=train[test.columns.values]\n\n#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1729)\nX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=1000)\n\n\n\nparams = {}\nparams[\'objective\'] = "reg:linear"\nparams[\'eta\'] = 0.05\nparams[\'max_depth\'] = 5\nparams[\'subsample\'] = 0.8\nparams[\'colsample_bytree\'] = 0.6\nparams[\'silent\'] = True\n\nprint (\'\')\n\ntest_preds = np.zeros(test.shape[0])\nxg_train = xgb.DMatrix(X_train, label=y_train)\nxg_test = xgb.DMatrix(X_test)\n\nwatchlist = [(xg_train, \'train\')]\nnum_rounds = 100\n\nxgclassifier = xgb.train(params, xg_train, num_rounds, watchlist, feval = evalerror, early_stopping_rounds= 20, verbose_eval = 10)\npreds = xgclassifier.predict(xg_test, ntree_limit=xgclassifier.best_iteration)\n\nprint (\'RMSLE Score:\', rmsle(y_test, preds))\n\nfxg_test = xgb.DMatrix(test)\nfold_preds = np.around(xgclassifier.predict(fxg_test, ntree_limit=xgclassifier.best_iteration), decimals = 1)\ntest_preds += fold_preds\n\nsubmission = pd.DataFrame({\'id\':ids, \'Demanda_uni_equil\': test_preds})\nsubmission.to_csv(\'submission.csv\', index=False)\''}, {'role': 'assistant', 'content': 'load_data'}]