[{'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.model_selection import KFold, cross_val_score\n\n## Create a dataframe to store all the models\' cross validation score\nevaluate = pd.DataFrame(columns=["model","cv","std"])\n\n\n## Encoded dataset\nfor name,model in pipelines:\n    kfold = KFold(n_splits=10,random_state=42)\n    cv = cross_val_score(model, X_train, y_train, cv=kfold, n_jobs=-1, scoring="neg_root_mean_squared_error")\n    \n    row = evaluate.shape[0]\n    evaluate.loc[row,"model"] = name\n    evaluate.loc[row,"cv"] = round(cv.mean(),3)\n    evaluate.loc[row,"std"] = "+/- {}".format(round(cv.std(),4))\n    \n    evaluate = evaluate.sort_values("cv",ascending=False)\n\nevaluate\''}, {'role': 'assistant', 'content': 'helper_functions'}]