A model predicted a forest fire correctly when it actually occurred. What is thiscase called?

When a model predicts no fire and there is actually no fire, it is called?

If a model predicts fire but there is no fire, this is:

Define Accuracy in model evaluation

Calculate accuracy if a model makes 70 correct predictions out of 100.

What problem arises if the same data is used for both training and evaluation?

What does Precision measure in model evaluation?

Recall is also known as:

How is the F1 score calculated?

A confusion matrix has True Positives, True Negatives, False Positives, and whatelse?

Case: An AI model classifies disease cases incorrectly as healthy. What is thiscalled?

Why is it important to split data into training and test sets?

When a model gives 80% correct results, what is the accuracy?

What type of problem does the confusion matrix typically summarize?

Case: An email spam detector labels too many non-spam mails as spam. This is an issueof:

Which metric balances Precision and Recall?

Case: A facial recognition model has high false positives. What is a possible risk?

Which metric indicates how many predicted positives are actually correct?

Case: A model designed to detect water shortage predicted shortage in 34 cases, butonly 22 were true shortages, and 12 were false alarms. Calculate Precision.

Case: In a test set, the model predicted 10 positives correctly, missed 3 positives, falsely alarmed 2 negatives as positive, and classified 85 negatives correctly. What is the recall?

Why is performance evaluation essential before deploying a model?

Why is splitting data into training, validation, and test sets beneficial?

What is the meaning of True Negative Rate?

Case: If a model never predicts positive, what would be its recall?

What is the role of the test data in model evaluation?