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Choosing N-1 Satisficing Metrics and One 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.
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When trading off N criteria for an ML model, how many should be set as satisficing metrics?
A satisficing metric requires meeting a minimum threshold rather than optimizing the value as much as possible.
When N-1 criteria are set as satisficing metrics, the remaining criterion is called the _____ metric.
When balancing N criteria for a model, how many should be set as satisficing metrics?
Satisficing metrics must only meet a specified threshold value, rather than being maximized.
When trading off N different criteria, you set _____ of them as satisficing metrics.
Match each term to its correct description in the satisficing/optimizing framework.
Order the steps for setting up the N-1 satisficing / one optimizing metric framework for a model.
Why does Machine Learning Yearning cite binary file size as a criterion worth constraining for mobile apps?
In this framework, you may designate more than one criterion as the optimizing metric.
In the Machine Learning Yearning example, binary file size and running time serve as _____ metrics.
Match each criterion from the Machine Learning Yearning mobile-app example to its role in the framework.
Order the steps for evaluating a single candidate model using the satisficing/optimizing framework.
How does the satisficing and optimizing framework simplify decision-making when trading off N different model evaluation criteria?
Defining satisficing and optimizing metrics for a mobile machine learning application
Clarifying the requirement for a satisficing metric in model evaluation