Question

A data engineer is building a BigQuery ML logistic regression model to predict ride-cancellation likelihood. Analysts note that cancellation behavior depends heavily on the *combination* of pickup_city and time_of_day (e.g., 'downtown at rush hour' behaves very differently from 'suburb at midnight'), rather than either feature alone. The engineer wants the model to learn these interaction effects directly during training without manually pre-joining string values in a staging table. Which approach should the engineer use inside the CREATE MODEL statement?

Accepted Answer

Apply ML.FEATURE_CROSS to pickup_city and time_of_day within the TRANSFORM clause so the crossed categorical feature is generated at both training and prediction time.

Answer

Concatenate pickup_city and time_of_day with CONCAT in the SELECT statement of a temporary table, then reference the resulting column as a feature.

Answer

Add both pickup_city and time_of_day as separate features and increase the number of training iterations so the model discovers the interaction.

Answer

Apply ML.BUCKETIZE to time_of_day and ML.STANDARD_SCALER to pickup_city inside the TRANSFORM clause to normalize the inputs before training.