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2. Explain the role of the “Reality” condition in model evaluation.
Ans. “Reality” represents the actual state of the area when the model makes predictions, crucial for
understanding accuracy.
3. Why is a True Positive outcome important in model evaluation?
Ans. True Positive indicates the model correctly predicted a positive reality, demonstrating the model’s
effectiveness.
4. How does a confusion matrix contribute to evaluating model performance?
Ans. A confusion matrix provides a summarized view of the model’s prediction outcomes, aiding in assessing
True Positive and Negative.
5. Briefly describe how Precision is calculated in model evaluation.
Ans. Precision is calculated as the ratio of True Positives to the sum of True Positives and False Positives,
emphasizing accurate positive predictions.
6. What does a False Negative indicate in the context of model evaluation?

Ans. A False Negative occurs when the model predicts a negative reality, but the actual condition is positive,
highlighting a potential area for improvement.
7. Define F1 Score and its significance in evaluating models.

Ans. F1 Score is the harmonic mean of Precision and Recall, offering a balanced measure of a model’s
performance, considering both false positives and negatives.

8. When might Accuracy alone be inadequate for model evaluation?
Ans. Accuracy may fall short when there is an imbalance in the dataset or when assessing the model’s
performance on previously unused data.
9. In case of predicting water shortage, explain the importance of a True Negative.
Ans. A True Negative signifies the model accurately predicting no water shortage when there is indeed no
shortage, showcasing the model’s specificity.
10. What aspect of model performance does recall measure?
Ans. Recall measures the ability of the model to capture true positives, assessing its sensitivity to positive
occurrences.

F. Long answer type question.
1. Explain the importance of using a separate test dataset for model evaluation.
Ans. Using a separate test dataset ensures that the model is assessed on data it has never encountered during
training, providing insights into its ability to generalize and perform well on new, unseen instances. This
process helps gauge the model’s reliability and effectiveness in real-world scenarios beyond its training
data.
2. How does the term “Reality” contribute to understanding the performance of an AI model, especially in
the context of flood predictions?

Ans. “Reality” represents the true condition of the area when the model makes predictions. In flood
predictions, it indicates whether there is an actual flood or not. Evaluating the model based on this
reality helps measure its accuracy and reliability, ensuring that predictions align with the true conditions
of the environment.
3. Discuss the significance of True Positives in model evaluation and provide an example scenario where it
holds particular importance.

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