A manufacturing company trains an XGBoost binary classifier in SageMaker to detect defective parts on an assembly line. Only 1.5% of parts are defective. The initial model achieves 98.5% accuracy but misses nearly all defective parts, which is unacceptable because undetected defects cause costly recalls. The team wants to improve the model's ability to catch defects while keeping the training pipeline simple and avoiding synthetic data generation. Which approach BEST addresses this problem?