通常，当线性回归器的训练在一次迭代之后停止时，这意味着存在一个问题，可能是由于模型过拟合所致。解决这个问题的一种方法是调整模型的超参数，如学习速率、正则化参数等。下面是一个示例代码，展示如何在BigQuery中为线性回归器设置超参数并重新训练模型：

CREATE MODEL mydataset.myregressormodel
OPTIONS
  (model_type='linear_reg',
   input_label_cols=['mylabelcolumn']) AS
SELECT myfeaturecolumn1, myfeaturecolumn2, ... , myfeaturecolumnN, mylabelcolumn
FROM mydataset.mytable
WHERE RAND() < 0.8;  -- training set

-- Change the hyperparameters of the model
ALTER MODEL mydataset.myregressormodel
SET OPTIONS
  (learn_rate=0.01,
   l1_reg=0.001,
   l2_reg=0.01);

-- Re-train the model with updated hyperparameters
CREATE OR REPLACE MODEL mydataset.myregressormodel
OPTIONS
  (model_type='linear_reg',
   input_label_cols=['mylabelcolumn']) AS
SELECT myfeaturecolumn1, myfeaturecolumn2, ... , myfeaturecolumnN, mylabelcolumn
FROM mydataset.mytable;

-- Evaluate the performance of the re-trained model
SELECT
  SQRT(mean_squared_error) AS rmse
FROM
  ML.EVALUATE(MODEL mydataset.myregressormodel,
              (
              SELECT myfeaturecolumn1, myfeaturecolumn2, ... , myfeaturecolumnN, mylabelcolumn
              FROM mydataset.mytable
              WHERE RAND() >= 0.8  -- evaluation set
              )
             );

在这个示例中，我们首先创建了一个线性回归模型，并使用“OPTIONS”子句指定了模型类型，输入特征列和标签列。然后，我们可以使用“ALTER MODEL”命