Page 307 - Ai Book - 10

8. What is the F1 Score a harmonic mean of?
a. Precision and Recall

b. Accuracy and Recall
c. True Positive and True Negative

d. Precision and True Positive
9. What is the perfect value for an F1 Score?
a. 0 b. 0.5

c. 1 d. 10
10. In which scenario might Accuracy alone be insufficient for model evaluation?
a. Predicting flood occurrence

b. Predicting water shortage in schools
c. Predicting unexpected rain
d. Predicting the outcome of a sports event

Answers
1. (b) 2. (b) 3. (a) 4. (b) 5. (c) 6. (a)

7. (c) 8. (a) 9. (c) 10. (b)

B. Fill in the blanks.
1. Model evaluation is the process of understanding the _________________ of an AI model by using a test
dataset that was not part of the training data.
2. In model evaluation, the term _________________ refers to the output generated by the machine.

3. The two conditions used to determine the efficiency of a model are _________________ and
_________________.

4. _________________ is an outcome where the model correctly predicts positive reality.
5. _________________ is the ratio of the correct number of predictions to the total number of predictions.
6. Precision is the ratio of True Positive to the sum of True Positive and _________________.
7. Recall is the ratio of True Positive to the sum of True Positive and _________________.
8. F1 Score is the harmonic mean of _________________ and _________________.

9. If Precision is low and Recall is low, then the F1 Score is _________________.
10. _________________ may not be enough to ensure the model’s performance on data that has never
been used.

Answers
1. reliability 2. prediction 3. Prediction, Reality 4. True Positive
5. Model Accuracy 6. False Positive 7. False Negative 8. Precision, Recall

9. low 10. Accuracy
C. State ‘T’ for True or ‘F’ for False statements.

1. The primary purpose of model evaluation is to train the AI model.
2. In model evaluation, the term “Reality” represents the training dataset.

181
181