[{'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 numpy import mean\nfrom numpy import std\nfrom sklearn.datasets import make_regression\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import RepeatedKFold\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.ensemble import StackingRegressor\nfrom matplotlib import pyplot\n\n# get a stacking ensemble of models\ndef get_stacking():\n    # define the base models\n    base_models = list()\n    base_models.append(('ridge', ridge_model))\n    base_models.append(('lasso', lasso_model))\n    base_models.append(('rf', rf_model_en))\n    # define meta learner model\n    learner = LinearRegression()\n    # define the stacking ensemble\n    model = StackingRegressor(estimators=base_models, final_estimator=learner, cv=10)\n    return model\n \n# get a list of models to evaluate\ndef get_models():\n    models = dict()\n    models['ridge'] = ridge_model\n    models['lasso'] = lasso_model\n    models['rf_en'] = rf_model_en\n    models['stacking'] = get_stacking()\n    return models\n \n# evaluate a given model using cross-validation\ndef evaluate_model(model, X, y):\n    cv = RepeatedKFold(n_splits=10, n_repeats=5, random_state=19)\n    scores = cross_val_score(model, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1, error_score='raise')\n    return scores\n\n# get the models to evaluate\nmodels = get_models()\n# evaluate the models and store results\nresults, names = list(), list()\nfor name, model in models.items():\n    scores = evaluate_model(model, X_train, y_train)\n    results.append(scores)\n    names.append(name)\n    print(f'{name} {mean(scores):.3f} {std(scores):.3f}')\n# plot model performance for comparison\npyplot.boxplot(results, labels=names, showmeans=True)\npyplot.show()"}, {'role': 'assistant', 'content': "['modelling']"}]