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When a project must satisfy more than one evaluation criterion, a common strategy is to set one metric as a minimum acceptable requirement and then optimize a different metric among the solutions that meet that requirement. For example, a team might require inference time to stay under a limit and then select the model with the highest accuracy.

Combining Metrics With a Threshold and an Objective

A satisficing metric is one that only needs to clear a preset threshold rather than be maximized as far as possible. For example, if a customer support tool must answer within 80 milliseconds to feel responsive, then response time can be used as a satisficing metric with an upper limit of 80 ms.

Acceptable-Threshold Metric

An optimizing metric is the main measure you use to compare candidate systems after any minimum requirements on other metrics have already been set. For example, in a loan-approval model, you might require the false positive rate to stay below a fixed limit and then choose the model with the highest recall among the remaining candidates.

Primary Optimization Metric

When a model must satisfy several criteria, one practical strategy is to turn most of them into minimum requirements and reserve a single metric for optimization. For example, in a speech-recognition system you might require latency under 100 ms and RAM use under 250 MB, then choose the model with the highest recognition accuracy among the candidates that meet those limits. This approach narrows the search to acceptable models and then ranks them by the main objective.

Using Hard Limits and One Score to Compare Models

For a device that listens for a spoken trigger phrase, both kinds of mistakes matter: missing the phrase and reacting when it was not spoken. A practical goal is to reduce the false negative rate, while also requiring that the false positive rate stay below one false alarm per 24 hours of use.

Using Two Error Rates in a Voice-Trigger Goal

When a team agrees on the metric it will optimize, it can make faster progress.

Shared Evaluation Metric Lets a Team Move Faster

Latency limit versus F1 score

What Counts as a Satisficing Metric

When several metrics matter, the one that must meet a minimum acceptable level is called a _____ metric.

Combining Multiple Evaluation Criteria

Choosing the Right Evaluation Metric

Applying Satisficing and Optimizing Metrics

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.

Explaining Optimizing and Satisficing Metrics

Case context:
You are selecting a handwriting recognition model for a portable scanner. You want the highest possible recognition accuracy, but the scanner can only load models that use 120 MB of RAM or less.

Question:
How should you treat accuracy and RAM usage when comparing candidate models? Specify which metric is optimizing and which is satisficing.

Sample answer:
RAM usage should be treated as a satisficing metric because the scanner has a hard 120 MB limit. Any model above that limit is unacceptable, while any model at or below it is acceptable from the memory standpoint. Accuracy should be the optimizing metric. Among the models that meet the RAM limit, choose the one with the best accuracy.

Key points:
- RAM usage is the satisficing metric.
- The acceptable threshold is 120 MB or less.
- Accuracy is the optimizing metric.
- Select the most accurate model among those that satisfy the RAM limit.

Rubric:
A correct response must identify the memory constraint as satisficing and accuracy as optimizing, with the decision rule of maximizing accuracy subject to the RAM cap.

Choosing a deployment metric under a hard device limit

Question:
In the example, why is response latency treated as a satisficing metric instead of an optimizing metric?

Sample answer:
Response latency is a satisficing metric because the system only needs to be fast enough to meet a practical threshold, such as responding within 150 ms. Once that standard is met, making it even faster is less important than improving the main quality goal.

Key points:
- It only needs to be good enough, not endlessly improved.
- It is judged against an acceptable threshold, such as 150 ms.

Rubric:
The answer should say that response latency only has to meet an acceptable threshold or be good enough, rather than being improved without limit.

Why Latency Is a Satisficing Metric

What does a satisficing metric ask for?

When a system has both an optimizing metric and a satisficing metric, the best plan is to maximize the optimizing metric even if the satisficing metric is violated.

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