Page 308 - Ai Book - 10

3. True Positive is an outcome where the model incorrectly predicts a positive reality.

4. A confusion matrix is a matrix used for evaluating the performance of a model based on three
parameters: Prediction, Reality, and Probability.
5. Model Accuracy is calculated as the ratio of True Positive to the total number of predictions.

6. Precision is the ratio of True Positive to the sum of True Positive and False Negative.
7. Recall is the ratio of True Positive to the sum of True Positive and False Positive.

8. F1 Score is the arithmetic mean of Precision and Recall.
9. If Precision is low and Recall is low, the F1 Score is high.

10. Accuracy alone is always sufficient to ensure the model’s performance on unseen data.
Answers

1. F 2. F 3. F 4. F 5. F 6. F
7. F 8. F 9. F 10. F

D. Very short answer type question.
1. What is the purpose of model evaluation?
Ans. To understand the reliability of an AI model by using a separate test dataset.
2. Define “Reality” in the context of model evaluation.
Ans. The real condition of the area when the prediction is made.
3. What does a True Positive outcome signify in model evaluation?

Ans. The model correctly predicts a positive reality.
4. Why is a confusion matrix used in model evaluation?
Ans. It summarizes the model’s prediction results based on parameters such as True Positive and Negative.
5. How is Precision calculated?
Ans. True Positive divided by the sum of True Positive and False Positive.
6. What does a False Negative in model evaluation indicate?

Ans. The model predicts a negative reality, but the reality is positive.
7. Define F1 Score.
Ans. The harmonic mean of Precision and Recall.
8. When might accuracy alone be insufficient for model evaluation?
Ans. When there is an imbalance in the dataset or when evaluating on unused data. Accuracy might be
insufficient
9. In case of predicting water shortage, what does a True Negative represent?
Ans. Model predicts no water shortage, and there is no water shortage.
10. What parameter does recall measure in model evaluation?

Ans. The ratio of True Positive to the sum of True Positive and False Negative.

E. Short answer type question.
1. What is the significance of evaluating an AI model?
Ans. Model evaluation helps assess the reliability and performance of the AI model by using a separate test
dataset.

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