A data scientist trains a Spark ML LogisticRegression model to predict customer churn (binary label). The dataset is heavily imbalanced, with only 8% of customers churning. They want to evaluate model performance in a way that is robust to this class imbalance and does not depend on a fixed classification threshold. Which evaluator and metric configuration should they use?