A data scientist has trained a Spark ML classification pipeline to predict one of five customer segments. The classes are moderately imbalanced. The scientist wants to evaluate the model on a held-out DataFrame using Spark ML's built-in evaluation tooling, choosing a metric that balances precision and recall across all classes. Which evaluator and metric configuration should they use?