Page 311 - Ai Book - 10

H. Assertive and reason-based questions.
1. Assertion: Precision is an important metric in evaluating AI models.

Reason: Precision measures the accuracy of positive predictions, ensuring a low rate of false positives.
a. True for both Assertion and Reason b. True for Assertion, False for Reason
c. False for Assertion, True for Reason d. False for both Assertion and Reason

2. Assertion: Accuracy alone may not be sufficient for evaluating a model’s performance.
Reason: Accuracy does not consider the real scenario in tasks like predicting water shortage.
a. True for both Assertion and Reason b. True for Assertion, False for Reason

c. False for Assertion, True for Reason d. False for both Assertion and Reason
3. Assertion: F1 Score is considered a balanced metric in model evaluation.
Reason: F1 Score takes into account both Precision and Recall, preventing the dominance of one
parameter over the other.

a. True for both Assertion and Reason b. True for Assertion, False for Reason
c. False for Assertion, True for Reason d. False for both Assertion and Reason
Answers
1. (c) 2. (c) 3. (a)

AI Assessment Zone

A. Tick () the correct answer.
1. What does a False Positive in a prediction scenario indicate?

a. Model predicts positive, and there is a positive outcome
b. Model predicts positive, but there is a negative outcome

c. Model predicts negative, and there is a positive outcome
d. Model predicts negative, but there is a negative outcome
2. How is Precision calculated?

a. True Positive / (True Positive + False Positive)
b. (True Positive + True Negative) / Total cases

c. True Positive / Total cases
d. True Positive / (True Positive + False Negative)

3. What does Recall measure in model evaluation?
a. The ratio of True Positive to False Positive

b. The ratio of True Positive to the sum of True Positive and False Positive
c. The ratio of True Positive to the sum of True Positive and False Negative
d. The ratio of False Positive to True Negative

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