The AI agrees with the suggested answer, which is D. High recall.

Reasoning:
The question states that the cost of false negative predictions is much higher than the cost of false positive predictions. Recall is the metric that focuses on minimizing false negatives. High recall means the model is good at identifying most of the actual positive cases. In this scenario, it is crucial to avoid missing positive cases (false negatives) because they are more costly. Therefore, the ML engineer should prioritize a model with high recall.

Why other options are not the best choice:

A. Low precision: Low precision means the model has a high number of false positives. While false positives are less costly than false negatives in this case, minimizing them is still desirable, and prioritizing low precision wouldn't make sense.
B. High precision: High precision indicates a low false positive rate. While this is a good characteristic, it doesn't directly address the more critical issue of minimizing false negatives. A model with high precision might still have a significant number of false negatives, which are costly.
C. Low recall: Low recall is the opposite of what we want. It indicates a high false negative rate, which is what we are trying to avoid most.

Prioritizing high recall directly addresses the primary concern outlined in the question: minimizing the costly false negative predictions.

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