Which metric is most appropriate for evaluating a model's performance on a multi

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Question: Which metric is most appropriate for evaluating a model\'s performance on a multi-class classification problem?

Options:

Correct Answer: Macro F1 Score

Solution:

Macro F1 Score calculates the F1 Score for each class independently and averages them, making it suitable for multi-class problems.

Which metric is most appropriate for evaluating a model's performance on a multi-class classification problem?

Which metric is most appropriate for evaluating a model's performance on a multi-class classification problem?

Step 1: Understand that a multi-class classification problem involves more than two classes (categories) to predict.
Step 2: Learn about the F1 Score, which is a measure that combines precision and recall to evaluate a model's performance.
Step 3: Recognize that in a multi-class setting, we can calculate the F1 Score for each class separately.
Step 4: The Macro F1 Score takes the F1 Score of each class, adds them together, and divides by the number of classes to get an average.
Step 5: Understand that using the Macro F1 Score is beneficial because it treats all classes equally, regardless of their size or number of instances.

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