[{'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:"""  CV  .<br>NFOLD = 5<br>folds = StratifiedKFold(n_splits= NFOLD, shuffle=True, random_state=2018)<br><br>total_score = 0<br>best_epoch = 0<br>for n_fold, (train_idx, valid_idx) in enumerate(folds.split(train_df, y_value)):<br>    train_x, train_y = train_df.iloc[train_idx], y_value.iloc[train_idx]<br>    valid_x, valid_y = train_df.iloc[valid_idx], y_value.iloc[valid_idx]<br><br>    history = model.fit(train_x.values, train_y.values, nb_epoch=30, batch_size = 64, validation_data=(valid_x.values, valid_y.values), <br>                        verbose=1, callbacks=callbacks)<br><br>    keras_history_plot(history)<br>    predict = model.predict(valid_x.values)<br>    null_count = np.sum(pd.isnull(predict) )<br>    if null_count > 0:<br>        print("Null Prediction Error: ", null_count)<br>        predict[pd.isnull(predict)] = predict[~pd.isnull(predict)].mean()<br><br>    cv_score = log_loss(valid_y, predict )<br>    total_score += cv_score<br>    best_epoch = max(best_epoch, np.max(history.epoch))<br>    print(\\\'Fold {} LogLoss : {}\\\'.format(n_fold + 1, cv_score ))<br><br>print("Best Epoch: ", best_epoch)<br>print("Total LogLoss", total_score/NFOLD)<br>print("Baseline model Score Diff", total_score/NFOLD - baseline_neuralnetwork_score)<br>"""'}, {'role': 'assistant', 'content': 'modelling'}]