Question

A data science team trains a BigQuery ML model to classify incoming support tickets into one of five priority categories. The categories are roughly balanced. After running ML.EVALUATE, they want a single metric that summarizes how often the model assigns the correct category across all five classes, treating each class equally. Which evaluation output should they focus on?

Accepted Answer

The accuracy value, which reflects the overall proportion of correctly classified tickets across the balanced classes

Answer

The roc_auc value, since it directly measures multi-class separability across the five categories

Answer

The log_loss value, because a lower log loss guarantees the highest number of correct category assignments

Answer

The mean_squared_error value returned by ML.EVALUATE for the classification model