标签: xgbregressor

当我使用 XGBoostRegressor 预测股票价格时，我尝试拟合该模型。

    # XGBoostRegressor\nparameters = {\n    'n_estimators': [100, 200, 300, 400],\n    'learning_rate': [0.001, 0.005, 0.01, 0.05],\n    'max_depth': [8, 10, 12, 15],\n    'gamma': [0.001, 0.005, 0.01, 0.02],\n    'random_state': [42]\n}\n\neval_set = [(X_train, y_train), (X_valid, y_valid)]\nmodel = xgb.XGBRegressor(eval_set = eval_set, objective = 'reg:squarederror', verbose = False)\nclf = GridSearchCV(model, parameters)\n\nclf.fit(X_train, y_train)\n\nprint(f'Best params: {clf.best_params_}')\nprint(f'Best validation score = {clf.best_score_}')\n

然后我收到一个警告。

Parameters: { "eval_set", "verbose" } might not be used.\n  This could be a false alarm, with some parameters getting used by language …