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Recall for a Positive-Class Detector
Recall is the percentage of all actual positive examples in the dev or test set that the classifier correctly labels as positive.
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Related
Precision for a Cat Detector
Recall for a Positive-Class Detector
One Score from Precision and Recall
Averaging Several Metrics into One Score
Using a Dev Set to Compare Model Versions
What is the main advantage of using one evaluation score while developing models?
A single score can help a team rank many models quickly.
Single-number metrics for model selection
Match each development choice to its role in testing model ideas.
Put the model-selection process with one metric in order.
Why a single evaluation score speeds model development
Use one primary score to compare many candidate models.
What two kinds of guidance does a single-number score provide?
What evaluation strategy best helps you choose quickly among many model candidates?
A single evaluation score can help a team choose among competing models and point the team toward the next improvement.
Learn After
What does the recall of a cat detector measure?
True or False: Recall is the fraction of positive predictions that turn out to be correct.
Recall in a binary classification task
Match each term to its meaning in a wildfire-detection recall example.
Order the steps to compute recall for a fox classifier on the test set.
Why Recall Does Not Fully Describe a Bird Detector
Estimate recall for a fox-image detector
Define recall for a bird classifier in one or two sentences.
Which examples are used to compute recall?
True or False: If a spam filter misses many spam emails, its recall will be lower.