Case context: You are building a wake word detection system for a smart speaker. You care about the model's accuracy, but the device strictly has only 50MB of RAM available to load your model into memory.

Question: Based on the principles of optimizing and satisficing metrics, how should you structure your evaluation metrics to decide which model is best to deploy? Specify which metric is optimizing and which is satisficing.

Sample answer: In this scenario, the memory footprint must be treated as a satisficing metric. Because the device strictly has 50MB of RAM, any model requiring more than 50MB is completely unacceptable, while any model under 50MB is 'good enough' to fit on the device. Accuracy, on the other hand, should be treated as the optimizing metric. Therefore, the evaluation strategy should be to maximize accuracy, subject to the classifier meeting the satisficing criterion of using 50MB of RAM or less.

Key points:

• Memory footprint/RAM is the satisficing metric.
• The satisficing criterion is set at 50MB or less.
• Accuracy is the optimizing metric.
• The goal is to maximize accuracy subject to the RAM constraint.

Rubric: A correct response will correctly identify RAM/memory footprint as the satisficing metric due to the strict threshold, and accuracy as the optimizing metric, demonstrating the relationship of maximizing one subject to the other.