Practice What Is A Confusion Matrix? (30.1) - Confusion Matrix - CBSE 10 AI (Artificial Intelleigence)

Practice: What is a Confusion Matrix?

Lesson 30.1Confusion Matrix

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8 questions

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4 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
2 questions · about 1 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
Define True Positive.
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Think about the examples used in emails.
Question 2.
What is a False Negative?
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Consider what happens when a spam email is missed.
3Question 3.
What does a True Positive indicate?
- Correctly predicted negative class
- Incorrectly predicted positive class
- Correctly predicted positive class
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Consider the meaning of true versus false.
4Question 4.
Is a False Positive a good indicator of model performance?
- True
- False
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Think of how it affects users.
Question 5.
Suppose a new spam detection model is tested on 500 emails, resulting in 200 True Positives, 50 False Positives, 100 True Negatives, and 150 False Negatives. Calculate accuracy, precision, recall, and F1 score.
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Make sure to use the correct formulas for each metric.
Question 6.
You have a dataset with 80 instances labeled positive and 20 labeled negative. The model predicts all instances as positive. Is this a good strategy? What is the confusion matrix and the derived metrics?
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Reflect on the implications of missing actual negative cases.

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