Practice Structure Of A Confusion Matrix (30.2) - Confusion Matrix - CBSE 10 AI (Artificial Intelleigence)

Practice: Structure of a Confusion Matrix

Lesson 30.2Confusion Matrix

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10 questions

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6 sample questions

Question 1.
What does 'True Positive' mean in a confusion matrix?
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Think about how the model identifies spam.
Question 2.
Which position in the matrix represents False Negatives?
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Look for what the model missed.
3Question 3.
What does a True Positive in a confusion matrix represent?
- Correct positive prediction
- Correct negative prediction
- Incorrect positive prediction
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Remember the definition of True Positive.
4Question 4.
True or False: A False Negative refers to cases where the model incorrectly predicts the negative class.
- True
- False
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Think about what the model misses.

Given the confusion matrix statistics below:

	Predicted Positive	Predicted Negative
Actual Positive	55 (TP)	15 (FN)
Actual Negative	10 (FP)	60 (TN)
How can you calculate Precision and Recall? Discuss which metric would be more important in a medical diagnosis context.

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Consider the consequences of missing actual positives in a medical setting.

Question 6.
If you have a dataset with 1,000 examples where 900 are negative and 100 are positive, how could a model with high accuracy still be inadequate based on the confusion matrix?
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Reflect on class distributions and implications on model performance.

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