What defines a satisficing metric in a machine learning evaluation?

A satisficing metric must be optimized to its best possible value rather than just meeting a threshold.

A satisficing metric only needs to be _____ by meeting a chosen value.

Match each satisficing metric term to its description from the running time example.

Order the steps for turning running time into a satisficing metric.

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.

Explain why treating running time as a satisficing metric simplifies classifier selection.

Case context:
A team is evaluating two classifiers. Classifier A runs in 80ms and achieves 90% accuracy. Classifier B runs in 60ms and achieves 88% accuracy. The team has set running time as a satisficing metric with an acceptable threshold of at most 100ms.

Question:
Using the satisficing metric rule for running time, which classifier should the team select, and why?

Sample answer:
Both Classifier A (80ms) and Classifier B (60ms) meet the satisficing threshold of at most 100ms, so running time is no longer a distinguishing factor between them. Since running time is only a satisficing metric and not something to be further optimized, the team should select the classifier with the better performance on the remaining metric, which is accuracy. Classifier A has higher accuracy (90% vs 88%), so the team should choose Classifier A.

Key points:
- Both classifiers meet the 100ms satisficing threshold for running time.
- A satisficing metric does not need to be minimized beyond meeting the threshold.
- Since running time is satisfied for both, the decision should be based on the other metric.
- Classifier A should be chosen because it has higher accuracy (90% vs 88%).

Rubric:
Full credit responses recognize that both classifiers satisfy the running time threshold, so running time does not need to be minimized further, and correctly select the classifier with higher accuracy. Partial credit for correctly identifying both classifiers meet the threshold but failing to select the higher-accuracy classifier.

Select a classifier using a running time satisficing threshold.

Question:
In one to three sentences, explain what it means for running time to be a satisficing metric with a 100ms threshold.

Sample answer:
It means that running time simply has to be 'good enough,' taking at most 100ms, rather than needing to be minimized as much as possible. As long as a classifier's running time is within the 100ms threshold, it satisfies the requirement, and running time does not need further optimization.

Key points:
- Running time only needs to be at most 100ms.
- Meeting this threshold is sufficient; further minimization is not required.
- This distinguishes a satisficing metric from an optimizing metric.

Rubric:
Full credit answers state that the classifier only needs to meet the 100ms threshold rather than be minimized further. Partial credit for mentioning the 100ms value without explaining the 'good enough' concept.

What does it mean for running time to be a satisficing metric with a 100ms threshold?

What running time value is used as the example satisficing threshold?

A satisficing metric simply requires meeting a certain value.

A satisficing metric only has to be good enough by meeting a chosen value. For example, if running in 100ms is acceptable, then running time can be used as a satisficing metric that should take at most 100ms.

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When multiple evaluation metrics matter, such as accuracy and running time of a learning algorithm, one way to combine them is to define an acceptable value for one metric and then optimize another metric subject to meeting that criterion. In the passage's example, running time is the satisficing metric and accuracy is the optimizing metric.

Optimizing and Satisficing Metrics

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Satisficing Metric

An optimizing metric is the evaluation metric to optimize after the satisficing metric criteria have been set. In the passage's classifier example, accuracy is the optimizing metric when accuracy is maximized subject to meeting the running time criterion.

Optimizing Metric

When trading off N different criteria, such as a model's binary file size, running time, and accuracy, one approach is to set N-1 criteria as satisficing metrics that must meet certain values, and define the final criterion as the optimizing metric. For example, one can set acceptable thresholds for binary file size and running time, then optimize accuracy given those constraints.

Choosing N-1 Satisficing Metrics and One Optimizing Metric

For a hardware device that uses a microphone to listen for a particular wakeword, both false positive rate and false negative rate matter. One reasonable performance goal is to minimize the false negative rate as the optimizing metric, subject to no more than one false positive every 24 hours of operation as the satisficing metric.

Wake Word Detection as Optimizing and Satisficing Example

Once a team is aligned on the evaluation metric to optimize, they will be able to make faster progress.

Team Alignment on Evaluation Metric Speeds Progress

Identifying Optimizing vs. Satisficing Metrics

Definition of Satisficing Metrics

Combining Multiple Evaluation _____ Metrics

Roles of Evaluation Metrics

Applying Satisficing and Optimizing Metrics

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.

Explaining Satisficing vs. Optimizing

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.

Diagnosing Algorithm Performance Constraints

Question:
In the passage's example, why is running time designated as a 'satisficing metric' rather than an optimizing metric?

Sample answer:
Running time is designated as a satisficing metric because the classifier does not need to be infinitely fast; it just needs to be 'good enough' to meet an acceptable running time criterion, such as taking at most 100ms.

Key points:
- It just has to be 'good enough'.
- It involves meeting an acceptable running time criterion (e.g., at most 100ms).

Rubric:
The answer should mention that running time only requires meeting an 'acceptable' threshold or being 'good enough' (e.g., at most 100ms), rather than needing to be maximized or minimized indefinitely.

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