Practice Recall (sensitivity) (12.3.2) - Evaluation Methodologies of AI Models

Practice: Recall (Sensitivity)

Lesson 12.3.2Evaluation Methodologies of AI Models

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9 questions

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2 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does recall measure?
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Think about how many of actual positives are captured.
Question 2.
What is the formula for calculating recall?
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Recall involves true positives and false negatives.
3Question 3.
What does recall measure in an AI model?
- The percentage of actual positives that are correctly predicted
- The overall accuracy of the model
- The number of true negatives
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Remember, recall is all about capturing positives!
4Question 4.
True or False: Recall is the same as precision.
- True
- False
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Recall emphasizes capturing highs, while precision assesses correctness of predictions.
Question 5.
In an AI model testing for a rare disease, there are 90 true positives, 10 false negatives, and 100 true negatives. Calculate the recall and explain its significance.
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Focus on true positives and false negatives for your calculation.
Question 6.
Discuss how to balance recall and precision in a machine learning model designed for fraud detection in banking. What are the implications of focusing on one over the other?
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Reflect on the customer experience versus fraud detection efficiency.

5 more questions in this section. Enroll free to practise them all.