Primary Optimization 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.
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Related
Acceptable-Threshold Metric
Primary Optimization Metric
Using Hard Limits and One Score to Compare Models
Using Two Error Rates in a Voice-Trigger Goal
Shared Evaluation Metric Lets a Team Move Faster
Latency limit versus F1 score
What Counts as a Satisficing Metric
Combining Multiple Evaluation Criteria
Choosing the Right Evaluation Metric
Applying Satisficing and Optimizing Metrics
Explaining Optimizing and Satisficing Metrics
Choosing a deployment metric under a hard device 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.
Learn After
What is the optimizing metric in this fraud-screening example?
True or False: Satisficing criteria are usually set before choosing the final optimizing metric.
What metric is maximized in the classifier example after meeting the speed requirement?
Match each metric role to its meaning in a mobile app ranking task.
Order the steps for choosing the optimization target in a mobile app example.
Distinguish an Optimizing Metric from a Satisficing Metric
Choose the Metric to Maximize After Setting a Time Limit
What is an optimizing metric in model selection?
When Does a Metric Become the Optimizing Metric?
For a mobile classifier that must answer within 150 milliseconds, inference speed is the metric to maximize and accuracy only has to clear a minimum cutoff.