An ML engineer is training a binary health-risk model (positive = likely at risk, negative = unlikely) with ages 30–60 included as a feature. The difference in proportions of labels (DPL) for the positive class is +0.9 for the 40–45 age group versus other ages, indicating overrepresentation. How should the engineer correct this imbalance?

The problem states that the difference in proportions of labels (DPL) for the positive class is +0.9 for the 40–45 age group, indicating an overrepresentation of positive examples in this specific group. To correct an overrepresentation, you should reduce the number of instances of the overrepresented class. Therefore, undersampling the positive examples in the 40–45 age group is the correct approach. Oversampling would exacerbate the existing imbalance. Undersampling other groups or oversampling the negative class for other groups would not directly address the overrepresentation within the 40-45 age group.