[{'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:from sklearn import linear_model, metrics, svm\nimport matplotlib\nimport matplotlib.pyplot as plt\n%matplotlib inline\nlogreg = linear_model.LogisticRegression(C = 1e30)\nlogreg.fit(train_df_x, train_df_y)\npred_lr = logreg.predict_proba(test_df_x)[:,1]\nfpr_lr, tpr_lr, thre_lr = metrics.roc_curve(test_df_y, pred_lr)\nroc_auc_lr = metrics.auc(fpr_lr, tpr_lr)\nmy_svm = svm.SVC(kernel='linear')\nmy_svm.fit(train_df_x, train_df_y)\npred_svm = my_svm.decision_function(test_df_x)\nfpr_svm, tpr_svm, thre_svm = metrics.roc_curve(test_df_y, pred_svm)\nroc_auc_svm = metrics.auc(fpr_svm, tpr_svm)\nfig = plt.figure(figsize = (6, 5))\nplt.title('ROC Curve')\nplt.xlabel('fpr')\nplt.ylabel('tpr')\nplt.xlim([0,1])\nplt.ylim([0,1])\nplt.plot([0,1],[0,1],'r--', label='')\nplt.plot(fpr_lr, tpr_lr, label='AUC log_reg({0:.3f})'.format(roc_auc_lr))\nplt.plot(fpr_svm, tpr_svm, label='AUC SVM({0:.3f})'.format(roc_auc_svm))\nplt.legend(loc=4)"}, {'role': 'assistant', 'content': 'modelling'}]