[{'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:scores = []\ntr_scores = []\nfor train_index, test_index in skf.split(train, y):\n    train_df = train.loc[train_index,new_col4train].reset_index(drop = True)\n    valid_df = train.loc[test_index,new_col4train].reset_index(drop = True)\n    train_y, valid_y = y[train_index], y[test_index]\n    te = TargetEncodingExpandingMean(columns_names=new_col4train)\n\n    X_tr = te.fit_transform(train_df, train_y)\n    X_val = te.transform(valid_df)\n    \n    te2 = TargetEncodingSmoothing(\n        columns_names= new_col4train,\n        k = 3, f = 1.5,\n    )\n    \n    X_tr2 = get_CV_target_encoding(train_df, train_y, te2, cv = 5)\n    te2.fit(train_df, train_y)\n    X_val2 = te2.transform(valid_df)\n    \n    X_tr = pd.concat([X_tr, X_tr2], axis = 1)\n    X_val = pd.concat([X_val, X_val2], axis = 1)\n\n    model = get_model()\n    model.fit(X_tr,train_y)\n\n    predictions = model.predict_proba(X_val)[:,1]\n    scores.append(roc_auc_score(valid_y, predictions))\n\n    train_preds = model.predict_proba(X_tr)[:,1]\n    tr_scores.append(roc_auc_score(train_y, train_preds))\n\nprint("Train AUC score: {:.4f} Valid AUC score: {:.4f}, STD: {:.4f}".format(\n    np.mean(tr_scores), np.mean(scores), np.std(scores)\n))'}, {'role': 'assistant', 'content': 'modelling'}]