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3 cards from this section. Quick, good the night before a test.
What does the F1 Score measure?
Think about why you need both precision and recall.
Is a higher F1 Score always better?
Consider what the score reflects.
What is the formula for calculating the F1 Score?
Think about how you would express both precision and recall together.
True or False: A high F1 Score indicates a model with both high precision and high recall.
Recall what makes the F1 score valuable.
You have a model that outputs false negatives primarily. How would you improve its recall without significantly affecting precision?
Think about how adjustments might impact both types of errors.
Discuss how selecting an F1 Score as your evaluation metric could impact model selection in a project aimed at fraud detection.
Consider how stakeholders value actual fraud detection versus false positives.
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