Question: Describe the difference between an optimizing metric and a satisficing metric when comparing machine learning models. Include one example showing how both can be used together.

Sample answer: An optimizing metric is the main quantity used to rank models. The goal is to make this metric as good as possible, either by increasing it or decreasing it depending on the task. A satisficing metric is a requirement that must clear a minimum acceptable level, but it is not the main quantity being optimized.

For example, suppose a team is choosing a speech recognition model for a mobile app. They want word accuracy to be as high as possible, but they also require inference time to stay under 80 milliseconds and memory use to stay below 250 MB. In this setup, accuracy is the optimizing metric. Latency and memory are satisficing metrics because each must remain within its limit. The team compares only models that satisfy the constraints, then selects the one with the best accuracy.

Key points:

• An optimizing metric is the primary score used to choose the best model.
• A satisficing metric is a constraint that must meet an acceptable threshold.
• The common strategy is to optimize one metric while requiring the others to stay within limits.
• A valid example should show one metric being maximized or minimized and another acting as a cutoff.

Rubric: A strong response should clearly separate the purpose of an optimizing metric from that of a satisficing metric and show how both are used together in a realistic model-selection example.