[{'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:# HYPEROPT\ndef auc_model(params):\n    params = {\n        "n_estimators": int(params["n_estimators"]),\n        "max_features": int(params["max_features"]),\n        "min_samples_leaf": int(params["min_samples_leaf"]),\n        "min_samples_split": int(params["min_samples_split"]),\n    }\n    clf = RandomForestRegressor(**params, random_state=96, n_jobs=-1)\n    return cross_val_score(\n        clf, X1, y1, cv=3, scoring="neg_mean_squared_log_error"\n    ).mean()\n\n\nparams_space = {\n    "n_estimators": hp.quniform("n_estimators", 0, 300, 50),\n    "max_features": hp.quniform("max_features", 1, 3, 1),\n    "min_samples_leaf": hp.quniform("min_samples_leaf", 1, 50, 1),\n    "min_samples_split": hp.quniform("min_samples_split", 1, 50, 1),\n}\nbest = 0\n\n\ndef f(params):\n    global best\n    auc = auc_model(params)\n    if auc > best:\n        print("New Best", best, params)\n    return {"loss": -auc, "status": STATUS_OK}\n\n\ntrials = Trials()\nbest = fmin(f, params_space, algo=tpe.suggest, max_evals=200, trials=trials)\nprint("best:\\\n", best)\''}, {'role': 'assistant', 'content': 'modelling'}]