Page 312 - Ai Book - 10

4. When is F1 Score considered ideal?

a. When Precision is low and Recall is low
b. When Precision is low and Recall is high

c. When Precision is high and Recall is low
d. When Precision is high and Recall is high

5. In Scenario 1, predicting water shortage in schools, what does a True Negative represent?
a. Model predicts water shortage, and there is water shortage
b. Model predicts no water shortage, and there is no water shortage

c. Model predicts water shortage, but there is no water shortage
d. Model predicts no water shortage, but there is water shortage

6. In Scenario 2, predicting unexpected rain, what does a False Negative indicate?
a. Model predicts rain, and there is rain

b. Model predicts no rain, but there is rain
c. Model predicts rain, but there is no rain

d. Model predicts no rain, and there is no rain
7. Which measure is calculated by the ratio of True Positive to the sum of True Positive and False Negative?
a. Accuracy b. Precision

c. Recall d. F1 Score
8. What does a confusion matrix summarize?

a. Training data b. Prediction results
c. Reality conditions d. Test dataset
9. When might Accuracy be misleading in model evaluation?

a. When Precision is low
b. When Recall is low

c. When there is an imbalance in the dataset
d. When the model is overfitting

10. How is the F1 Score affected when Precision is high and Recall is low?
a. F1 Score is high

b. F1 Score is low
c. F1 Score remains the same

d. F1 Score cannot be determined

B. Fill in the blanks.
1. The formula for Precision is True Positive divided by the sum of True Positive and ________________.
2. If Precision is high and Recall is low, then the F1 Score is ________________.

3. A confusion matrix is a summary of model ________________.
4. F1 Score’s perfect value is ________________.

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