Question: Explain why treating running time as a satisficing metric, rather than trying to optimize it directly, simplifies the process of choosing the best classifier.

Sample answer: Treating running time as a satisficing metric simplifies classifier selection because it removes the need to weigh running time against other metrics like accuracy when comparing classifiers. Instead of trying to find the classifier with the absolute fastest running time, the team only needs to check whether a classifier's running time meets the chosen acceptable threshold, such as 100ms. Once that condition is met, running time no longer factors into the comparison, and the team can focus on optimizing the other metric to select the best classifier.

Key points:

• A satisficing metric only needs to meet a chosen acceptable value, e.g., 100ms.
• Once the threshold is met, the metric no longer needs further optimization.
• This avoids having to trade off running time against other metrics.
• It simplifies comparing classifiers by reducing the number of metrics being actively optimized.

Rubric: Full credit responses explain that a satisficing metric only needs to meet a fixed threshold rather than be maximized or minimized, and that this removes the need to trade it off against other metrics when comparing classifiers. Partial credit for mentioning the threshold concept without explaining the simplification benefit.