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S Solvedolved QQuestionsuestions

A. Tick () the correct answer.
1. What is the primary purpose of model evaluati on?
a. To train the AI model b. To understand the reliability of the model

c. To create confusion matrices d. To generate predicti ons

2. What does “Reality” represent in the context of model evaluati on?
a. Model output b. The real event for which the predicti on is made

c. Test dataset d. Training dataset
3. What outcome is termed as True Positi ve in a fl ood predicti on scenario?

a. Model predicts fl ood, and there is a fl ood
b. Model predicts no fl ood, and there is no fl ood

c. Model predicts fl ood, but there is no fl ood
d. Model predicts no fl ood, but there is a fl ood

4. What does a confusion matrix summarise in model evaluati on?
a. Training data vs Testi ng data b. Predicti on results vs Reality

c. Reality events vs Imaginary events d. Environment under which model is tested
5. How is model accuracy calculated?

a. True Positi ves/Total cases
b. True Positi ves/(True Positi ves + False Positi ves)

c. (True Positi ves + True Negati ves)/Total cases
d. False Positi ves/(True Positi ves + False Negati ves)

6. When is Precision considered high?
a. When True Positi ve are high b. When False Positi ve are high

c. When False Negati ve are high d. When True Negati ve are high
7. What does Recall measure in model evaluati on?

a. The rati o of True Positi ves to False Positi ves
b. The rati o of True Positi ves to the sum of True Positi ves and False Positi ves

c. The rati o of True Positi ves to the sum of True Positi ves and False Negati ves

d. The rati o of False Positi ves to True Negati ves
8. The F1 Score is a harmonic mean of:
a. Precision and Recall b. Accuracy and Recall

c. Precision and Accuracy d. Accuracy and Error

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