Question

A data science team at a retail company is training a boosted tree classifier in BigQuery ML to predict customer churn. Their initial model underperforms, and they suspect the default values for parameters like the number of trees, learning rate, and max tree depth are suboptimal. They want to systematically search across ranges of these parameters within BigQuery ML without exporting the data or building a separate Vertex AI pipeline. What is the most appropriate approach?

Accepted Answer

Use CREATE MODEL with NUM_TRIALS and the hyperparameter search ranges (e.g., HPARAM_RANGE and HPARAM_CANDIDATES) so BigQuery ML runs built-in hyperparameter tuning across trials.

Answer

Train several separate models manually, each with a different hardcoded combination of parameters, then compare their evaluation metrics with ML.EVALUATE.

Answer

Export the training data to Vertex AI and use Vertex AI Vizier as the only supported way to tune BigQuery model parameters.

Answer

Use the TRANSFORM clause to automatically select the best learning rate and tree depth during preprocessing.