[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:# CV interations\n\nroc_auc = list()\naverage_precision = list()\noof = np.zeros(len(X))\nbest_iteration = list()\n\nfor train_idx, test_idx in skf.split(X, y):\n    X_train, y_train = X.iloc[train_idx, :], y[train_idx]\n    X_test, y_test = X.iloc[test_idx, :], y[test_idx]\n    \n    train = Pool(data=X_train, \n             label=y_train,            \n             feature_names=list(X_train.columns),\n             cat_features=categorical_variables)\n\n    test = Pool(data=X_test, \n                label=y_test,\n                feature_names=list(X_test.columns),\n                cat_features=categorical_variables)\n\n    catb.fit(train,\n             verbose_eval=100, \n             early_stopping_rounds=50,\n             eval_set=test,\n             use_best_model=True,\n             #task_type = "GPU",\n             plot=False)\n    \n    best_iteration.append(catb.best_iteration_)\n    preds = catb.predict_proba(X_test)\n    \n    oof[test_idx] = preds[:,1]\n    \n    roc_auc.append(roc_auc_score(y_true=y_test, y_score=preds[:,1]))\n    average_precision.append(average_precision_score(y_true=y_test, y_score=preds[:,1]))'}, {'role': 'assistant', 'content': "['modelling']"}]