Page 306 - Ai Book - 10

S Solvedolved QQuestionsuestions

A. Tick () the correct answer.
1. What is the primary purpose of model evaluation?
a. To train the AI model

b. To understand the reliability of the model
c. To create confusion matrices

d. To generate predictions
2. What does “Reality” represent in the context of model evaluation?
a. Model output
b. The real condition when the prediction is made

c. Test dataset
d. Training dataset

3. What outcome is termed as True Positive in a flood prediction scenario?
a. Model predicts flood, and there is a flood
b. Model predicts no flood, and there is no flood

c. Model predicts flood, but there is no flood
d. Model predicts no flood, but there is a flood
4. What does a confusion matrix summarize in model evaluation?

a. Training data b. Prediction results
c. Reality conditions d. Test dataset
5. How is model accuracy calculated?

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

c. (True Positive + True Negative) / Total cases
d. False Positive / (True Positive + False Negative)
6. When is Precision considered high?
a. When True Positive are high

b. When False Positive are high
c. When False Negative are high

d. When True Negative are high
7. 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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