Question

A data science team is training a BigQuery ML linear regression model on a sales dataset. About 8% of rows have NULL values in the numeric feature `avg_basket_value`. They want the imputation logic applied automatically and identically during both training and prediction, without maintaining separate preprocessing SQL for inference. Which approach should they use?

Accepted Answer

Wrap an ML.IMPUTER(avg_basket_value, 'mean') expression inside the TRANSFORM clause of the CREATE MODEL statement.

Answer

Run an UPDATE statement to replace NULLs with the column mean in the source table before every training and prediction job.

Answer

Create a view that uses IFNULL(avg_basket_value, 0) and train the model against that view, then query the view at prediction time.

Answer

Set the model option AUTO_IMPUTE=TRUE so BigQuery ML fills missing values automatically.