Question

A retail analytics team has trained a logistic regression model in BigQuery ML to predict which customers will churn. Business stakeholders want to understand, for each individual prediction, which features pushed the customer toward or away from churning so they can design targeted retention offers. The team wants to stay entirely within BigQuery using SQL and avoid exporting the model. Which approach should they use?

Accepted Answer

Run ML.EXPLAIN_PREDICT on the model to obtain per-prediction feature attributions alongside the predicted labels

Answer

Run ML.GLOBAL_EXPLAIN after enabling global explanations during training to see per-customer feature contributions

Answer

Run ML.EVALUATE to produce a confusion matrix and precision/recall values for each customer

Answer

Export the model to Vertex AI and enable Vertex Explainable AI with sampled Shapley to get per-row attributions