Question

A data scientist is training a BigQuery ML logistic regression model to predict customer churn. In the training table, only about 2% of the rows represent churned customers. After training, the model reports very high accuracy but almost never predicts the positive (churn) class, making it useless for the business. The team wants to improve the model's ability to identify the minority class while continuing to use BigQuery ML. What is the most appropriate action?

Accepted Answer

Set the AUTO_CLASS_WEIGHTS option to TRUE in the CREATE MODEL statement so class weights are balanced inversely to frequency.

Answer

Increase the MAX_ITERATIONS option so the model trains longer and eventually learns the minority class.

Answer

Switch the model_type to LINEAR_REG since regression handles rare events better than classification.

Answer

Remove the L2_REG regularization so the model can memorize the rare churn examples.