Practice F1 Score (12.3.3) - Evaluation Methodologies of AI Models - CBSE 12 AI (Artificial Intelligence)

Practice: F1 Score

Lesson 12.3.3Evaluation Methodologies of AI Models

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9 questions

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3 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.
Define the F1 Score in your own words.
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Think about how it relates to precision and recall.
Question 2.
Explain why the F1 Score might be more useful than accuracy in certain models.
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Consider scenarios where one class might dominate another.
3Question 3.
What does the F1 Score primarily assess?
- Balance between precision and recall
- Overall accuracy
- Model speed
- Data size
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Focus on how precision and recall relate to F1 Score.
4Question 4.
True or False: The F1 Score can be misleading in cases of imbalanced datasets.
- True
- False
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Think about the purpose of the F1 Score.
Question 5.
Consider a binary classifier with TP = 90, FP = 10, FN = 20. Fully calculate precision, recall, and F1 Score. What would happen if the focus leaned only on recall at the cost of precision?
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Balance between the two is critical!
Question 6.
Analyze a scenario in a fraud detection system where precision is high but recall is low. Discuss the risks involved and how F1 Score can guide improvements.
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Consider the implications of missed fraud cases.

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