[{'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:from sklearn.metrics import matthews_corrcoef, make_scorer, roc_auc_score, roc_curve\nMatthew = make_scorer(matthews_corrcoef)\n\nresults = []\n\ndef plot_custom_roc_curve(clf_name, y_true, y_scores):\n    auc_score = np.round(roc_auc_score(y_true, y_scores), 3)\n    fpr, tpr, thresholds = roc_curve(y_true, y_scores)\n    plt.plot(fpr, tpr, linewidth=2, label=clf_name+" (AUC Score: {})".format(str(auc_score)))\n    plt.plot([0, 1], [0, 1], \'k--\') # Dashed diagonal\n    plt.axis([0, 1, 0, 1])\n    plt.xlabel("FPR", fontsize=16)\n    plt.ylabel("TPR", fontsize=16)\n    plt.legend()\n    \n    \ndef performance_measures(model, X_tr=X_train_transformed, y_tr=y_train, X_ts=X_test_transformed, y_ts=y_test,\n                         store_results=True):\n    train_mcc = cross_val_score(model, X_tr, y_tr, scoring=Matthew, cv=kf, n_jobs=-1)\n    test_mcc = cross_val_score(model, X_ts, y_ts, scoring=Matthew, cv=kf, n_jobs=-1)\n    print("Mean Train MCC: {}\\\nMean Test MCC: {}".format(train_mcc.mean(), test_mcc.mean()))\n\n    \n    train_roc_auc = cross_val_score(model, X_tr, y_tr, scoring=\'roc_auc\', cv=kf, n_jobs=-1)\n    test_roc_auc = cross_val_score(model, X_ts, y_ts, scoring=\'roc_auc\', cv=kf, n_jobs=-1)\n    print("Mean Train ROC AUC Score: {}\\\nMean Test ROC AUC Score: {}".format(train_roc_auc.mean(), test_roc_auc.mean()))\n    \n    if store_results:\n        results.append([model.__class__.__name__, np.round(np.mean(train_roc_auc), 3), np.round(np.mean(test_roc_auc), 3), np.round(np.mean(train_mcc), 3), np.round(np.mean(test_mcc), 3)])\''}, {'role': 'assistant', 'content': "['helper_functions', 'comment_only']"}]