Question: Analyze the difference between an optimizing metric and a satisficing metric in the context of evaluating a machine learning algorithm. Provide an example to illustrate how they are combined.

Sample answer: When evaluating an algorithm with multiple goals, an optimizing metric is the primary performance measure that you actively seek to maximize or minimize. In contrast, a satisficing metric acts as a constraint or threshold; the algorithm only needs to be 'good enough' to meet an acceptable value on this metric. For example, if you care about accuracy and running time, you might define running time as a satisficing metric that must be under 100ms. Once that criterion is met, you optimize accuracy, meaning you select the model with the highest accuracy that still runs in under 100ms.

Key points:

• Optimizing metric is maximized or minimized.
• Satisficing metric only needs to meet an acceptable, 'good enough' threshold.
• They are combined by maximizing the optimizing metric subject to the satisficing metric's criteria.
• Example provided (e.g., maximizing accuracy while keeping running time under 100ms).

Rubric: A strong answer should clearly distinguish between maximizing a metric (optimizing) and merely meeting an acceptable threshold for a metric (satisficing), and accurately relate this to a combined evaluation strategy using a practical example.