[{'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.ensemble import AdaBoostClassifier\nada = AdaBoostClassifier()\nada_smote = AdaBoostClassifier()\nada.fit(X_train, y_train)\nada_smote.fit(X_smote, y_smote)\ny_predict = ada.predict_proba(X_test)[:, 1]\ny_predict_smote = ada_smote.predict_proba(X_test)[:, 1]\nprint(\'confusion matrix:\')\nprint(confusion_matrix(y_test, ada.predict(X_test)))\nprint(\'confusion matrix(smote):\' )\nprint(confusion_matrix(y_test, ada_smote.predict(X_test)))\nfpr, tpr, thresholds = roc_curve(y_test, y_predict)\nroc_auc = auc(fpr, tpr)\nfpr_smote, tpr_smote, thresholds_smote = roc_curve(y_test, y_predict_smote)\nroc_auc_smote = auc(fpr_smote, tpr_smote)\nplt.plot(fpr, tpr, label=\'ROC curve (area = %0.3f)\' % roc_auc)\nplt.plot(fpr_smote, tpr_smote, label=\'SMOTE curve (area = %0.3f)\' % roc_auc_smote)\nplt.plot([0, 1], [0, 1], \'k--\')  # random predictions curve\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.0])\nplt.xlabel(\'False Positive Rate or (1 - Specifity)\')\nplt.ylabel(\'True Positive Rate or (Sensitivity)\')\nplt.title(\'ROC for Adaboost\')\nplt.legend(loc="lower right")\nplt.savefig(\'ada.eps\')'}, {'role': 'assistant', 'content': 'modelling'}]