[{'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 score(params):<br>#     print("Training with params : ")<br>#     print(params)<br>#     num_round = 25  # int(params[\'n_estimators\'])<br>#     # del params[\'n_estimators\']<br>#     dtrain = xgb.DMatrix(X_train, label=y_train)<br>#     dvalid = xgb.DMatrix(X_test, label=y_test)<br>#     model = xgb.train(params, dtrain, num_round)<br>#     predictions = model.predict(dvalid)<br>#     score = mae(y_test, predictions)<br>#     br = \'-\'*130<br>#     print(f\'{br}\\<br>\\\\tScore of MAE: {score}\\<br>{br}\')<br>#     return {\'loss\': score, \'status\': STATUS_OK}<br><br># def optimize(trials):<br>#     space = {<br>#              \'n_estimators\' : hp.quniform(\'n_estimators\', 100, 1000, 1),<br>#              \'eta\' : hp.quniform(\'eta\', 0.4, 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\', 0.5, 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\': \'mae\',<br>#              \'objective\': \'reg:linear\',<br>#              \'nthread\': 4,<br>#              \'silent\' : 1<br>#              }<br><br>#     best = fmin(score, space, algo=tpe.suggest, trials=trials, max_evals=50)<br><br>#     print(best)<br>    <br># trials = Trials()<br># optimize(trials)'}, {'role': 'assistant', 'content': 'helper_functions'}]