[{'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 train_test_split\n\n\ndef linear_models(X_model, y_model, model_name):\n\n    X_train, X_test, y_train, y_test = train_test_split(X_model, y_model, random_state = 42)\n    if model_name == "ridge":\n        model = linear_model.Ridge(alpha = 0.1, random_state = 42).fit(X_train, y_train)\n    if model_name == "lasso":\n        model = linear_model.Lasso(alpha = 0.5, random_state = 42).fit(X_train, y_train)\n    if model_name == "xgb":\n        model = xgb.XGBRegressor(objective =\'reg:linear\', colsample_bytree = 0.3, learning_rate = 0.1,\n                max_depth = 5, alpha = 0.1, n_estimators = 10, random_state = 42).fit(X_train, y_train)\n        print(model.get_booster().get_score(importance_type="gain"))\n    if model_name == "catboost":\n        model = CatBoostRegressor(num_leaves = 31,random_state = 42,learning_rate = 0.05).fit(X_train, y_train)\n    predictions = model.predict(X_test)\n    return predictions, y_test\''}, {'role': 'assistant', 'content': 'modelling'}]