[{'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.ensemble import RandomForestClassifier\nrf = RandomForestClassifier()\nrf_smote = RandomForestClassifier()\nrf.fit(X_train, y_train)\nrf_smote.fit(X_smote, y_smote)\ny_predict = rf.predict_proba(X_test)[:, 1]\ny_predict_smote = rf_smote.predict_proba(X_test)[:, 1]\nprint(\'confusion matrix:\')\nprint(confusion_matrix(y_test, rf.predict(X_test)))\nprint(\'confusion matrix(smote):\' )\nprint(confusion_matrix(y_test, rf_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 RF\')\nplt.legend(loc="lower right")\nplt.savefig(\'rf.eps\')'}, {'role': 'assistant', 'content': "['helper_functions', 'evaluation', 'modelling', 'prediction']"}]