[{'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 xgboost as xgb<br>from hyperopt import hp<br>from hyperopt import fmin, tpe, hp, STATUS_OK, Trials<br><br>def train(index, train, hp_selection=False):<br>    train_store = train[index]<br>    X = train_store[train_store.columns.drop([\'Sales\', \'Store\', \'Customers\'])]<br>    y = train_store[\'Sales\']<br><br>    train_size = int(X.shape[0]*.99)<br>    print(f\'Regressor for {index} store\\<br>Training on {X.shape[0]} samples\')<br>    X_train, y_train = X.iloc[:train_size], y.iloc[:train_size]<br>    X_test, y_test = X.iloc[train_size:], y.iloc[train_size:]<br><br>    xtrain = xgb.DMatrix(X_train, np.log(y_train.values) + 1)<br>    xtest = xgb.DMatrix(X_test, np.log(y_test.values) + 1)<br>    <br>    if hp_selection:<br>        def score(params):<br>            num_round = 200<br>            model = xgb.train(params, xtrain, num_round, feval=rmspe_xg)<br>            predictions = model.predict(xtest)<br>            score = rmspe(y=y_test, yhat=predictions)<br>            return {\'loss\': score, \'status\': STATUS_OK}<br><br>        def optimize(trials):<br>            space = {<br>                     \'n_estimators\' : hp.quniform(\'n_estimators\', 1, 1000, 1),<br>                     \'eta\' : hp.quniform(\'eta\', 0.2, 0.825, 0.025),<br>                     \'max_depth\' : hp.choice(\'max_depth\', np.arange(1, 14, dtype=int)),<br>                     \'min_child_weight\' : hp.quniform(\'min_child_weight\', 1, 6, 1),<br>                     \'subsample\' : hp.quniform(\'subsample\', 0.7, 1, 0.05),<br>                     \'gamma\' : hp.quniform(\'gamma\', 0.5, 1, 0.05),<br>                     \'colsample_bytree\' : hp.quniform(\'colsample_bytree\', 0.5, 1, 0.05),<br>                     \'eval_metric\': \'rmse\',<br>                     \'objective\': \'reg:linear\',<br>                     \'nthread\': 4,<br>                     \'silent\' : 1<br>                     }<br><br>            best = fmin(score, space, algo=tpe.suggest, trials=trials, max_evals=250)<br>            return best<br>        <br>        trials = Trials()<br>        best_opts = optimize(trials)<br>        best_opts[\'silent\'] = 1<br>    else:<br>        best_opts = {\'colsample_bytree\': 0.7, <br>                  \'eta\': 0.625, <br>                  \'gamma\': 0.8, <br>                  \'max_depth\': 6,<br>                  \'eval_metric\': \'rmse\',<br>                  \'min_child_weight\': 6.0, <br>                  \'n_estimators\': 8.0,  # 585<br>                  \'silent\': 1,<br>                  \'nthread\': 4,<br>                  \'subsample\': 0.95}<br>        <br>    watchlist = [(xtrain, \'train\'), (xtest, \'eval\')]<br>    num_round = 10000<br>    regressor = xgb.train(best_opts, xtrain, num_round, watchlist, feval=rmspe_xg,<br>                          verbose_eval=10, early_stopping_rounds=50)<br>    print("Validating")<br>    train_probs = regressor.predict(xtest)<br>    indices = train_probs < 0<br>    train_probs[indices] = 0<br>    error = rmspe(np.exp(train_probs) - 1, y_test.values)<br>    print(\'error\', error)<br>    regressor = xgb.train(best_opts, xtest, 10, feval=rmspe_xg, xgb_model=regressor)<br>    return regressor'}, {'role': 'assistant', 'content': 'modelling'}]