[{'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:## Parameters\nparams = {\n    "alpha" : [.01, .1, .5, .7, .9, .95, .99, 1, 5, 10, 20],\n    "fit_intercept" : [True, False],\n    "normalize" : [True,False],\n    "solver" : [\'svd\', \'cholesky\', \'lsqr\', \'sparse_cg\', \'sag\', \'saga\'],\n    "tol" : [0.0001, 0.001, 0.01, 0.1],\n    "random_state" : [42]\n}\n\n## Ridge\nridge = Ridge()\nridge_grid = GridSearchCV(ridge, params, scoring=\'r2\', cv=5, n_jobs=-1)\nridge_grid.fit(X_train, y_train)\n\n## Output\nprint("Best parameters:  {}:".format(ridge_grid.best_params_))\nprint("Best score: {}".format(ridge_grid.best_score_))\n\n## Append to list\nbest_estimators.append(["Ridge",ridge_grid.best_estimator_])\''}, {'role': 'assistant', 'content': 'helper_functions'}]