Question

A fintech company wants to build a binary classification model in Vertex AI AutoML Tabular to detect fraudulent transactions. Only 0.8% of the labeled transactions are fraudulent. The team's priority is to catch as many actual fraud cases as possible while keeping false positives at a manageable level, and they have limited ML expertise. Which approach should they configure to best meet this goal?

Accepted Answer

Train the AutoML Tabular model and set the optimization objective to maximize the area under the precision-recall curve (AU PRC).

Answer

Train the AutoML Tabular model with the default optimization objective of maximizing the area under the ROC curve (AU ROC).

Answer

Manually downsample the majority class to a 50/50 balance before training and optimize for overall accuracy.

Answer

Switch to a BigQuery ML logistic regression model and optimize for log loss since AutoML cannot handle imbalanced data.