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3 cards from this section. Quick, good the night before a test.
Define what recall is in the context of model evaluation.
Think about how many actual spam were caught by the model.
What does precision refer to in evaluating models?
Consider the accuracy of the positive predictions made by the model.
What is recall?
It's about how well the model recognizes real spam emails.
True or False: A high recall score means the model is performing well.
Consider how many false positives undermine the effectiveness of a model.
A company wants to enhance its spam detection model to improve the user experience. They notice a shift where user reports of spam emails have doubled. Analyze potential reasons and suggest improvements based on evaluation metrics.
Look at the balance between true positives and false positives.
Given a list of 50 spam emails and 100 legitimate emails, if the model classifies 40 of the spam correctly but labels 20 legitimate emails as spam, calculate precision, recall, and F1 Score.
Calculate one step at a time for accurate results.
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